--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\ba72loko\projects\replication paper ZfVP\calculations\Study_2_log_file.log
  log type:  text
 opened on:  10 Jul 2023, 13:45:58

. 
. 
. *** Loading dataset
. use "data\Study_2\original_data.dta", clear //opening replication dataset

. describe

Contains data from data\Study_2\original_data.dta
 Observations:         4,740                  
    Variables:            61                  17 Nov 2019 08:11
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Variable      Storage   Display    Value
    name         type    format    label      Variable label
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
year            int     %8.0g                 
ccode           float   %9.0g                 
marshallsum     byte    %8.0g                 
milcoupsum      byte    %8.0g                 
milcoupsucces~m byte    %8.0g                 
troops          float   %9.0g                 
milexp          float   %9.0g                 
exsol           float   %9.0g                 
polity2         byte    %8.0g                 
durable         int     %8.0g                 
conflict        byte    %8.0g                 
gdpcap          float   %9.0g                 
chgdppc_l       float   %9.0g                 
elf             float   %9.0g                 
effective_num~r float   %9.0g                 
aggdemand       float   %9.0g                 
pko100          byte    %8.0g                 
pko100lag       byte    %8.0g                 
pko500          byte    %8.0g                 
pko500lag       byte    %8.0g                 
polity2lag      byte    %8.0g                 
conflictlag     byte    %8.0g                 
durablelag      int     %8.0g                 
population2000  long    %12.0g                
continent       byte    %8.0g                 
milcoup         byte    %8.0g                 
coupmarshall3   byte    %8.0g                 
coupmarshall3~g byte    %8.0g                 
ln_exsol        float   %9.0g                 
coupsum_mil_p~l byte    %8.0g                 
milcoupsucces~1 byte    %8.0g                 milcoupsuccess_mm.1
twothousands    byte    %8.0g                 
milcoupsucces~2 byte    %8.0g                 
milcoupfailed~2 byte    %8.0g                 
milcouplag2     byte    %8.0g                 
troopslag2      float   %9.0g                 
polity2lag2     byte    %8.0g                 
milcouplag      byte    %8.0g                 
robust_democr~3 float   %9.0g                 
robust_democr~4 float   %9.0g                 
robust_democr~5 float   %9.0g                 
robust_democr~6 float   %9.0g                 
robust_democr~7 float   %9.0g                 
robust_autocr~3 float   %9.0g                 
robust_autocr~4 float   %9.0g                 
robust_autocr~5 float   %9.0g                 
robust_autocr~6 float   %9.0g                 
robust_autocr~7 float   %9.0g                 
robust_anocra~3 float   %9.0g                 
robust_anocra~4 float   %9.0g                 
robust_anocra~5 float   %9.0g                 
robust_anocra~6 float   %9.0g                 
robust_anocra~2 float   %9.0g                 
robust_anocra~1 float   %9.0g                 
aut_troops      float   %9.0g                 
ano_troops      float   %9.0g                 
dem_troops      float   %9.0g                 
aut_troops_ro~d float   %9.0g                 
ano_troops_ro~d float   %9.0g                 
dem_troops_ro~d float   %9.0g                 
milcoupsum_cu~e float   %9.0g                 
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Sorted by: ccode  year

. 
end of do-file

. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. 
. *------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------*
. * Generating variables
. *------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------*
. **generating New robust democracy dummy variable**
. gen new_robust_democracy6 = . //generating a new variable for the fixed version of robust_democracy6
(4,740 missing values generated)

. replace new_robust_democracy6 = robust_democracy6
(4,740 real changes made)

. replace new_robust_democracy6 = 0 if polity2 == . //missings von polity2 als new_robust_autocracy6 == 0 codiert 
(1,214 real changes made)

. 
. 
. *------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------*
. * Labeling of varlabels
. *------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------*
. label variable marshallsum "All coup attempts"

. label variable milcoupsum "Military coup attempts"

. label variable milcoupsuccess_mm "Successful military coups"

. label variable troops "Peacekeepers"

. label variable robust_autocracy6 "Robust autocracy"

. label variable robust_anocracy5 "Anocracy"

. label variable robust_democracy6 "Robust democracy"

. label variable population2000 "Population"

. label variable gdpcap "GDP per capita"

. label variable durable "Regime durability"

. label variable conflict "Political violence"

. label variable milexp "Military expenditure"

. label variable exsol "Expenditure per soldier"

. label variable effective_number "Effective organizations"

. label variable elf "Ethnic fractionalization"

. label variable aut_troops "Autocracy Peacekeepers"

. label variable ano_troops "Anocracy Peacekeepers"

. label variable dem_troops "Democracy Peacekeepers"

. label variable aut_troops_round "Autocracy Peacekeepers rounded"

. label variable ano_troops_round "Anocracy Peacekeepers rounded"

. label variable dem_troops_round "Democracy Peacekeepers rounded"

. label variable new_robust_democracy6 "New robust democracy"

. 
. 
. *------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------*
. * Main Model
. *------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------*
. *************************************************************************************************Table B1 Supplementary Appendix ***********************************************************************
. *Table 1: Poisson regression, new robus democracy variable as IV, with year fixed effects
. 
. est clear

. 
. eststo: poisson milcoupsum robust_autocracy6##c.troops population2000 gdpcap durable conflict milexp exsol i.year, cluster(ccode)

Iteration 0:   log pseudolikelihood =   -474.314  
Iteration 1:   log pseudolikelihood = -384.68232  
Iteration 2:   log pseudolikelihood = -379.37312  
Iteration 3:   log pseudolikelihood = -377.32161  
Iteration 4:   log pseudolikelihood = -377.19739  
Iteration 5:   log pseudolikelihood = -377.19592  
Iteration 6:   log pseudolikelihood = -377.19563  
Iteration 7:   log pseudolikelihood = -377.19557  
Iteration 8:   log pseudolikelihood = -377.19555  

Poisson regression                                    Number of obs =    3,001
                                                      Wald chi2(30) = 13833.37
Log pseudolikelihood = -377.19555                     Prob > chi2   =   0.0000

                                              (Std. err. adjusted for 148 clusters in ccode)
--------------------------------------------------------------------------------------------
                           |               Robust
                milcoupsum | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------------+----------------------------------------------------------------
       1.robust_autocracy6 |   .3474264   .3130541     1.11   0.267    -.2661483    .9610011
                    troops |  -1.758985    .990541    -1.78   0.076     -3.70041    .1824396
                           |
robust_autocracy6#c.troops |
                        1  |   2.116678   1.002514     2.11   0.035     .1517879    4.081569
                           |
            population2000 |  -5.43e-09   6.96e-09    -0.78   0.435    -1.91e-08    8.21e-09
                    gdpcap |  -.0222085   .0498859    -0.45   0.656     -.119983     .075566
                   durable |  -.0226208   .0211666    -1.07   0.285    -.0641066     .018865
                  conflict |   .8681871   .3512962     2.47   0.013     .1796591    1.556715
                    milexp |  -338.2984   181.8823    -1.86   0.063    -694.7812    18.18433
                     exsol |   1.126818   1.524295     0.74   0.460    -1.860745    4.114382
                           |
                      year |
                     1992  |   -.200221   .4719885    -0.42   0.671    -1.125301    .7248595
                     1993  |  -1.129265   .6390107    -1.77   0.077    -2.381703    .1231729
                     1994  |  -.9485946   .6330356    -1.50   0.134    -2.189322    .2921324
                     1995  |  -.5200474   .4856852    -1.07   0.284    -1.471973    .4318781
                     1996  |  -.5127087   .5097367    -1.01   0.314    -1.511774    .4863568
                     1997  |  -1.493356    .583986    -2.56   0.011    -2.637948   -.3487647
                     1998  |  -2.145085    .766307    -2.80   0.005    -3.647019   -.6431505
                     1999  |  -1.181931   .5475014    -2.16   0.031    -2.255014    -.108848
                     2000  |  -1.101654   .5323014    -2.07   0.038    -2.144945   -.0583623
                     2001  |  -1.262487   .6802212    -1.86   0.063    -2.595696    .0707219
                     2002  |  -2.654042   1.065481    -2.49   0.013    -4.742347   -.5657369
                     2003  |  -1.254559   .6240738    -2.01   0.044    -2.477721   -.0313966
                     2004  |  -15.38926    .401024   -38.37   0.000    -16.17525   -14.60327
                     2005  |  -1.889177   .6057669    -3.12   0.002    -3.076458   -.7018956
                     2006  |  -1.019777    .584519    -1.74   0.081    -2.165413    .1258592
                     2007  |  -2.551027   .9035257    -2.82   0.005    -4.321904    -.780149
                     2008  |  -.8992441   .7196039    -1.25   0.211    -2.309642    .5111536
                     2009  |  -1.106929   .6741385    -1.64   0.101    -2.428216     .214358
                     2010  |  -1.008465   .7144559    -1.41   0.158    -2.408773    .3918429
                     2011  |  -2.022829   1.080325    -1.87   0.061    -4.140226    .0945692
                     2012  |  -.3212003   .6802842    -0.47   0.637    -1.654533    1.012132
                           |
                     _cons |  -1.598449   .4862834    -3.29   0.001    -2.551547   -.6453508
--------------------------------------------------------------------------------------------
(est1 stored)

. 
. estat gof //testing the fit of the poisson model

         Deviance goodness-of-fit =  569.9198
         Prob > chi2(2970)        =    1.0000

         Pearson goodness-of-fit  =  2669.468
         Prob > chi2(2970)        =    1.0000

. 
. eststo: poisson milcoupsum robust_anocracy5##c.troops population2000 gdpcap durable conflict milexp exsol i.year, cluster(ccode)

Iteration 0:   log pseudolikelihood =  -391.4553  
Iteration 1:   log pseudolikelihood = -383.10975  
Iteration 2:   log pseudolikelihood = -379.32684  
Iteration 3:   log pseudolikelihood = -376.91746  
Iteration 4:   log pseudolikelihood = -376.81701  
Iteration 5:   log pseudolikelihood = -376.81609  
Iteration 6:   log pseudolikelihood = -376.81589  
Iteration 7:   log pseudolikelihood = -376.81585  
Iteration 8:   log pseudolikelihood = -376.81584  

Poisson regression                                     Number of obs =   3,001
                                                       Wald chi2(30) = 8737.95
Log pseudolikelihood = -376.81584                      Prob > chi2   =  0.0000

                                             (Std. err. adjusted for 148 clusters in ccode)
-------------------------------------------------------------------------------------------
                          |               Robust
               milcoupsum | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------------------+----------------------------------------------------------------
       1.robust_anocracy5 |   .7817316   .2594659     3.01   0.003     .2731878    1.290275
                   troops |   .1522107   .1988262     0.77   0.444    -.2374814    .5419028
                          |
robust_anocracy5#c.troops |
                       1  |  -2.930493   .7362464    -3.98   0.000    -4.373509   -1.487476
                          |
           population2000 |  -3.97e-09   6.56e-09    -0.61   0.545    -1.68e-08    8.89e-09
                   gdpcap |  -.0167871    .043144    -0.39   0.697    -.1013479    .0677736
                  durable |  -.0144761   .0198038    -0.73   0.465    -.0532909    .0243387
                 conflict |   .7976088   .3172764     2.51   0.012     .1757584    1.419459
                   milexp |  -382.1884   181.3122    -2.11   0.035    -737.5538   -26.82308
                    exsol |   1.336649   1.394345     0.96   0.338    -1.396216    4.069515
                          |
                     year |
                    1992  |  -.2048054   .4699467    -0.44   0.663    -1.125884    .7162732
                    1993  |  -1.201046    .632525    -1.90   0.058    -2.440773    .0386797
                    1994  |  -1.010702   .6265837    -1.61   0.107    -2.238783    .2173798
                    1995  |   -.619668    .487503    -1.27   0.204    -1.575156    .3358204
                    1996  |  -.5857538   .4988413    -1.17   0.240    -1.563465    .3919571
                    1997  |  -1.544337   .5982173    -2.58   0.010    -2.716821   -.3718524
                    1998  |  -2.285881   .7647253    -2.99   0.003    -3.784715   -.7870469
                    1999  |  -1.329344   .5427933    -2.45   0.014    -2.393199   -.2654886
                    2000  |  -1.293675   .5320162    -2.43   0.015    -2.336408   -.2509427
                    2001  |  -1.513984   .6594338    -2.30   0.022    -2.806451   -.2215174
                    2002  |  -2.869951   1.085347    -2.64   0.008    -4.997193   -.7427091
                    2003  |  -1.452439   .6180022    -2.35   0.019    -2.663701   -.2411768
                    2004  |  -16.12357   .3960668   -40.71   0.000    -16.89985    -15.3473
                    2005  |  -2.037049   .5980494    -3.41   0.001    -3.209204   -.8648937
                    2006  |  -1.325479   .5988226    -2.21   0.027     -2.49915   -.1518085
                    2007  |  -2.635413   1.033337    -2.55   0.011    -4.660717   -.6101091
                    2008  |  -.9908033   .6588186    -1.50   0.133    -2.282064    .3004574
                    2009  |  -1.369943   .6596404    -2.08   0.038    -2.662814   -.0770711
                    2010  |  -1.294938   .7163836    -1.81   0.071    -2.699024    .1091481
                    2011  |  -2.279744    1.09334    -2.09   0.037    -4.422651   -.1368382
                    2012  |  -.5448664   .6740059    -0.81   0.419    -1.865894     .776161
                          |
                    _cons |  -1.883731   .4172994    -4.51   0.000    -2.701623   -1.065839
-------------------------------------------------------------------------------------------
(est2 stored)

. 
. estat gof //testing the fit of the poisson model

         Deviance goodness-of-fit =  569.1603
         Prob > chi2(2970)        =    1.0000

         Pearson goodness-of-fit  =  2813.733
         Prob > chi2(2970)        =    0.9801

. 
. eststo: poisson milcoupsum robust_democracy6##c.troops population2000 gdpcap durable conflict milexp exsol i.year, cluster(ccode)

Iteration 0:   log pseudolikelihood = -385.30408  
Iteration 1:   log pseudolikelihood = -378.49581  
Iteration 2:   log pseudolikelihood = -375.24587  
Iteration 3:   log pseudolikelihood = -373.13801  
Iteration 4:   log pseudolikelihood = -373.07491  
Iteration 5:   log pseudolikelihood = -373.07404  
Iteration 6:   log pseudolikelihood = -373.07385  
Iteration 7:   log pseudolikelihood = -373.07381  
Iteration 8:   log pseudolikelihood =  -373.0738  

Poisson regression                                     Number of obs =   3,001
                                                       Wald chi2(30) = 8652.34
Log pseudolikelihood = -373.0738                       Prob > chi2   =  0.0000

                                              (Std. err. adjusted for 148 clusters in ccode)
--------------------------------------------------------------------------------------------
                           |               Robust
                milcoupsum | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------------+----------------------------------------------------------------
       1.robust_democracy6 |  -1.233484   .3848285    -3.21   0.001    -1.987735   -.4792344
                    troops |  -.1543723   .3870665    -0.40   0.690    -.9130086     .604264
                           |
robust_democracy6#c.troops |
                        1  |  -.4891277   .5904536    -0.83   0.407    -1.646395    .6681401
                           |
            population2000 |  -3.04e-09   6.42e-09    -0.47   0.636    -1.56e-08    9.55e-09
                    gdpcap |  -.0078074   .0298136    -0.26   0.793    -.0662411    .0506263
                   durable |  -.0205296   .0206891    -0.99   0.321    -.0610794    .0200202
                  conflict |   .7093504   .3137122     2.26   0.024     .0944858    1.324215
                    milexp |  -348.2272   166.2554    -2.09   0.036    -674.0819   -22.37258
                     exsol |   .8459835   1.533316     0.55   0.581     -2.15926    3.851227
                           |
                      year |
                     1992  |  -.1799123    .466459    -0.39   0.700    -1.094155    .7343306
                     1993  |  -1.111726   .6362058    -1.75   0.081    -2.358667     .135214
                     1994  |  -.8926369   .6220897    -1.43   0.151     -2.11191    .3266364
                     1995  |   -.542125   .4732209    -1.15   0.252    -1.469621    .3853709
                     1996  |  -.5253178   .4966491    -1.06   0.290    -1.498732    .4480966
                     1997  |  -1.487289   .5774415    -2.58   0.010    -2.619053   -.3555244
                     1998  |  -2.190386   .7630204    -2.87   0.004    -3.685879   -.6948938
                     1999  |  -1.203542    .535083    -2.25   0.024    -2.252285   -.1547987
                     2000  |  -1.157645     .52102    -2.22   0.026    -2.178826   -.1364646
                     2001  |  -1.313838   .6760268    -1.94   0.052    -2.638826    .0111504
                     2002  |  -2.675284   1.068164    -2.50   0.012    -4.768848   -.5817205
                     2003  |  -1.252785   .6125597    -2.05   0.041     -2.45338   -.0521902
                     2004  |    -15.717   .3877856   -40.53   0.000    -16.47704   -14.95695
                     2005  |  -1.746964   .5900215    -2.96   0.003    -2.903385   -.5905432
                     2006  |  -1.007078   .5809118    -1.73   0.083    -2.145644    .1314882
                     2007  |  -2.328517   1.051082    -2.22   0.027    -4.388601   -.2684335
                     2008  |  -.6570954     .65678    -1.00   0.317    -1.944361    .6301698
                     2009  |  -1.074256   .6636811    -1.62   0.106    -2.375047    .2265348
                     2010  |  -.9739916   .7028171    -1.39   0.166    -2.351488    .4035047
                     2011  |  -1.970144   1.101736    -1.79   0.074    -4.129507    .1892201
                     2012  |  -.2671558   .6811879    -0.39   0.695     -1.60226    1.067948
                           |
                     _cons |  -1.298313   .4248104    -3.06   0.002    -2.130926   -.4656996
--------------------------------------------------------------------------------------------
(est3 stored)

. 
. estat gof //testing the fit of the poisson model

         Deviance goodness-of-fit =  561.6763
         Prob > chi2(2970)        =    1.0000

         Pearson goodness-of-fit  =  3167.701
         Prob > chi2(2970)        =    0.0059

. 
. eststo: poisson milcoupsum new_robust_democracy6##c.troops population2000 gdpcap durable conflict milexp exsol i.year, cluster(ccode)

Iteration 0:   log pseudolikelihood = -387.72275  
Iteration 1:   log pseudolikelihood = -381.05754  
Iteration 2:   log pseudolikelihood = -377.77238  
Iteration 3:   log pseudolikelihood = -375.53793  
Iteration 4:   log pseudolikelihood = -375.47456  
Iteration 5:   log pseudolikelihood = -375.47367  
Iteration 6:   log pseudolikelihood = -375.47348  
Iteration 7:   log pseudolikelihood = -375.47344  
Iteration 8:   log pseudolikelihood = -375.47343  

Poisson regression                                     Number of obs =   3,001
                                                       Wald chi2(30) = 8686.11
Log pseudolikelihood = -375.47343                      Prob > chi2   =  0.0000

                                                  (Std. err. adjusted for 148 clusters in ccode)
------------------------------------------------------------------------------------------------
                               |               Robust
                    milcoupsum | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------------------------+----------------------------------------------------------------
       1.new_robust_democracy6 |  -1.108746   .3937836    -2.82   0.005    -1.880548   -.3369445
                        troops |  -.1288217   .3725482    -0.35   0.730    -.8590028    .6013595
                               |
new_robust_democracy6#c.troops |
                            1  |  -.5571352   .6505744    -0.86   0.392    -1.832238    .7179673
                               |
                population2000 |  -2.92e-09   6.40e-09    -0.46   0.648    -1.55e-08    9.62e-09
                        gdpcap |  -.0097187   .0321368    -0.30   0.762    -.0727057    .0532682
                       durable |  -.0185098   .0198802    -0.93   0.352    -.0574743    .0204546
                      conflict |   .7104872     .31907     2.23   0.026     .0851215    1.335853
                        milexp |  -361.0201    175.802    -2.05   0.040    -705.5857   -16.45451
                         exsol |   .9453657   1.438614     0.66   0.511    -1.874267    3.764998
                               |
                          year |
                         1992  |  -.1858376   .4663328    -0.40   0.690    -1.099833     .728158
                         1993  |  -1.115243   .6369458    -1.75   0.080    -2.363634     .133148
                         1994  |  -.8965117   .6235488    -1.44   0.151    -2.118645    .3256214
                         1995  |  -.5412889   .4738989    -1.14   0.253    -1.470114    .3875358
                         1996  |  -.5206751   .4976592    -1.05   0.295    -1.496069     .454719
                         1997  |  -1.482248   .5784632    -2.56   0.010    -2.616015   -.3484807
                         1998  |  -2.185406   .7637233    -2.86   0.004    -3.682276   -.6885362
                         1999  |  -1.199833   .5354157    -2.24   0.025    -2.249228   -.1504372
                         2000  |  -1.158351   .5208029    -2.22   0.026    -2.179106   -.1375955
                         2001  |  -1.338814   .6771997    -1.98   0.048    -2.666101    -.011527
                         2002  |   -2.68816    1.06862    -2.52   0.012    -4.782618   -.5937028
                         2003  |  -1.290712   .6123261    -2.11   0.035    -2.490849   -.0905752
                         2004  |  -15.75532   .3837379   -41.06   0.000    -16.50743   -15.00321
                         2005  |  -1.804979   .5920026    -3.05   0.002    -2.965282   -.6446749
                         2006  |  -1.069061   .5801126    -1.84   0.065    -2.206061    .0679388
                         2007  |  -2.392854   1.050674    -2.28   0.023    -4.452137    -.333571
                         2008  |  -.7299687   .6533301    -1.12   0.264    -2.010472    .5505347
                         2009  |  -1.154156   .6639111    -1.74   0.082    -2.455398    .1470857
                         2010  |  -1.043341   .6998146    -1.49   0.136    -2.414952    .3282704
                         2011  |  -2.065577   1.095305    -1.89   0.059    -4.212336    .0811818
                         2012  |  -.3733699   .6803906    -0.55   0.583    -1.706911    .9601711
                               |
                         _cons |  -1.332866   .4257707    -3.13   0.002    -2.167361   -.4983708
------------------------------------------------------------------------------------------------
(est4 stored)

. 
. estat gof //testing the fit of the poisson model

         Deviance goodness-of-fit =  566.4755
         Prob > chi2(2970)        =    1.0000

         Pearson goodness-of-fit  =  3054.332
         Prob > chi2(2970)        =    0.1373

. 
. esttab using "tables\Study_2\Table_B1.rtf", b(3) se(3) star(+ 0.1 * 0.05 ** 0.01 *** 0.001) ///
> title(Table B1. Determinants of military coup attempts, 1991–2013; Poisson regression with year fixed effects, with new democracy dummy variable) ///
> drop(*.year 0.robust_autocracy6 0.robust_autocracy6#c.troops 0.robust_anocracy5 0.robust_anocracy5#c.troops ///
> 0.robust_democracy6 0.robust_democracy6#c.troops 0.new_robust_democracy6 0.new_robust_democracy6#c.troops) ///
> label varlabels(1.robust_autocracy6 "Robust autocracy" 1.robust_autocracy6#c.troops "Robust autocracy * Peacekeepers" ///
> 1.robust_anocracy5 "Anocracy" 1.robust_anocracy5#c.troops "Anocracy * Peacekeepers" 1.robust_democracy6 "Robust democracy" ///
> 1.robust_democracy6#c.troops "Robust democracy * Peacekeepers" 1.new_robust_democracy6 "New robust democracy" ///
> 1.new_robust_democracy6#c.troops "New robust democracy * Peacekeepers" _cons "Constant") ///
> order(1.robust_autocracy6 1.robust_autocracy6#c.troops 1.robust_anocracy5 1.robust_anocracy5#c.troops 1.robust_democracy6 ///
> 1.robust_democracy6#c.troops 1.new_robust_democracy6 1.new_robust_democracy6#c.troops troops gdpcap durable conflict milexp exsol population2000) ///
> nonumbers modelwidth(6) eqlabel(none) varwidth(33) replace
(output written to tables\Study_2\Table_B1.rtf)

. 
. est clear

. 
. *------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------*
. * Figures
. *------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------*
. 
. ************************************************************************************************FIGURE B1 and B2 Supplementary Appendix ****************************************************************
. est clear

. 
. gen troops_round = .
(4,740 missing values generated)

. replace troops_round = troops if troops != .
(3,588 real changes made)

. replace troops_round = round(troops_round, 1)
(1,610 real changes made)

. 
. 
. *model 1a: autocracy rounded
. poisson milcoupsum robust_autocracy6##c.troops_round population2000 gdpcap durable conflict milexp exsol, cluster(ccode)

Iteration 0:   log pseudolikelihood = -466.32061  
Iteration 1:   log pseudolikelihood = -409.93573  
Iteration 2:   log pseudolikelihood = -403.76225  
Iteration 3:   log pseudolikelihood = -401.80279  
Iteration 4:   log pseudolikelihood = -401.71862  
Iteration 5:   log pseudolikelihood = -401.71809  
Iteration 6:   log pseudolikelihood = -401.71809  

Poisson regression                                      Number of obs =  3,001
                                                        Wald chi2(9)  = 103.29
Log pseudolikelihood = -401.71809                       Prob > chi2   = 0.0000

                                                    (Std. err. adjusted for 148 clusters in ccode)
--------------------------------------------------------------------------------------------------
                                 |               Robust
                      milcoupsum | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------------------+----------------------------------------------------------------
             1.robust_autocracy6 |   .5596436   .3111707     1.80   0.072    -.0502397    1.169527
                    troops_round |  -1.869233   .9156049    -2.04   0.041    -3.663785   -.0746799
                                 |
robust_autocracy6#c.troops_round |
                              1  |   2.098962     .90212     2.33   0.020     .3308396    3.867085
                                 |
                  population2000 |  -2.79e-09   6.61e-09    -0.42   0.673    -1.57e-08    1.02e-08
                          gdpcap |  -.0191865    .047073    -0.41   0.684    -.1114479    .0730749
                         durable |  -.0228556    .022602    -1.01   0.312    -.0671546    .0214434
                        conflict |   .9614879   .3762145     2.56   0.011      .224121    1.698855
                          milexp |   -386.839   182.9476    -2.11   0.034    -745.4098   -28.26817
                           exsol |   .9229046   2.226189     0.41   0.678    -3.440347    5.286156
                           _cons |   -2.74936   .3784127    -7.27   0.000    -3.491036   -2.007685
--------------------------------------------------------------------------------------------------

. margins, dydx(robust_autocracy6) post

Average marginal effects                                 Number of obs = 3,001
Model VCE: Robust

Expression: Predicted number of events, predict()
dy/dx wrt:  1.robust_autocracy6

-------------------------------------------------------------------------------------
                    |            Delta-method
                    |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
--------------------+----------------------------------------------------------------
1.robust_autocracy6 |   .0325632   .0163319     1.99   0.046     .0005533    .0645732
-------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. est store m1a

. 
. *model 1b: autocracy rounded year fixed effects
. poisson milcoupsum robust_autocracy6##c.troops_round population2000 gdpcap durable conflict milexp exsol i.year, cluster(ccode)

Iteration 0:   log pseudolikelihood = -489.00073  
Iteration 1:   log pseudolikelihood = -418.21473  
Iteration 2:   log pseudolikelihood = -384.52179  
Iteration 3:   log pseudolikelihood =  -377.9073  
Iteration 4:   log pseudolikelihood = -376.48239  
Iteration 5:   log pseudolikelihood = -376.42605  
Iteration 6:   log pseudolikelihood =  -376.4251  
Iteration 7:   log pseudolikelihood = -376.42489  
Iteration 8:   log pseudolikelihood = -376.42484  
Iteration 9:   log pseudolikelihood = -376.42483  

Poisson regression                                    Number of obs =    3,001
                                                      Wald chi2(30) = 14337.04
Log pseudolikelihood = -376.42483                     Prob > chi2   =   0.0000

                                                    (Std. err. adjusted for 148 clusters in ccode)
--------------------------------------------------------------------------------------------------
                                 |               Robust
                      milcoupsum | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------------------+----------------------------------------------------------------
             1.robust_autocracy6 |   .3601162   .3143717     1.15   0.252     -.256041    .9762734
                    troops_round |  -1.899142    .947492    -2.00   0.045    -3.756193   -.0420922
                                 |
robust_autocracy6#c.troops_round |
                              1  |   2.291427     .97606     2.35   0.019     .3783848     4.20447
                                 |
                  population2000 |  -6.14e-09   7.16e-09    -0.86   0.391    -2.02e-08    7.89e-09
                          gdpcap |  -.0218337   .0496624    -0.44   0.660    -.1191702    .0755027
                         durable |  -.0226959   .0213816    -1.06   0.288    -.0646031    .0192113
                        conflict |   .8890898   .3528516     2.52   0.012     .1975133    1.580666
                          milexp |    -341.29    182.878    -1.87   0.062    -699.7243    17.14427
                           exsol |   1.141064   1.517681     0.75   0.452    -1.833537    4.115665
                                 |
                            year |
                           1992  |  -.2215799   .4716885    -0.47   0.639    -1.146072    .7029125
                           1993  |  -1.134162   .6384145    -1.78   0.076    -2.385431    .1171077
                           1994  |  -.9579494   .6326002    -1.51   0.130    -2.197823    .2819241
                           1995  |  -.5403067   .4877851    -1.11   0.268    -1.496348    .4157346
                           1996  |  -.5102004   .5099894    -1.00   0.317    -1.509761    .4893605
                           1997  |  -1.500069   .5839914    -2.57   0.010    -2.644671   -.3554665
                           1998  |  -2.152051   .7659853    -2.81   0.005    -3.653355   -.6507473
                           1999  |   -1.19353    .547582    -2.18   0.029    -2.266772   -.1202894
                           2000  |  -1.092906   .5317686    -2.06   0.040    -2.135154   -.0506592
                           2001  |  -1.281159   .6792764    -1.89   0.059    -2.612517     .050198
                           2002  |  -2.671551   1.065423    -2.51   0.012    -4.759741   -.5833613
                           2003  |   -1.26976   .6228583    -2.04   0.041     -2.49054   -.0489805
                           2004  |  -15.67837   .4033937   -38.87   0.000      -16.469   -14.88773
                           2005  |  -1.939687   .6063406    -3.20   0.001    -3.128093   -.7512813
                           2006  |  -1.075583   .5817731    -1.85   0.064    -2.215838    .0646708
                           2007  |  -2.671787   .9073312    -2.94   0.003    -4.450123   -.8934502
                           2008  |  -.9805522   .7122029    -1.38   0.169    -2.376444    .4153399
                           2009  |  -1.173223   .6711637    -1.75   0.080     -2.48868    .1422333
                           2010  |  -1.049443   .7125562    -1.47   0.141    -2.446028    .3471411
                           2011  |  -2.052127    1.07519    -1.91   0.056    -4.159461    .0552058
                           2012  |  -.3628285    .676272    -0.54   0.592    -1.688297    .9626403
                                 |
                           _cons |  -1.602391     .49076    -3.27   0.001    -2.564263   -.6405195
--------------------------------------------------------------------------------------------------

. margins, dydx(robust_autocracy6) post

Average marginal effects                                 Number of obs = 3,001
Model VCE: Robust

Expression: Predicted number of events, predict()
dy/dx wrt:  1.robust_autocracy6

-------------------------------------------------------------------------------------
                    |            Delta-method
                    |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
--------------------+----------------------------------------------------------------
1.robust_autocracy6 |   .0253939   .0142919     1.78   0.076    -.0026177    .0534055
-------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. est store m1b

. 
. *model 2a: autocracy
. poisson milcoupsum robust_autocracy6##c.troops population2000 gdpcap durable conflict milexp exsol, cluster(ccode)

Iteration 0:   log pseudolikelihood = -449.22112  
Iteration 1:   log pseudolikelihood = -410.06911  
Iteration 2:   log pseudolikelihood = -404.01836  
Iteration 3:   log pseudolikelihood = -402.11343  
Iteration 4:   log pseudolikelihood = -402.01812  
Iteration 5:   log pseudolikelihood = -402.01764  
Iteration 6:   log pseudolikelihood = -402.01764  

Poisson regression                                      Number of obs =  3,001
                                                        Wald chi2(9)  =  82.80
Log pseudolikelihood = -402.01764                       Prob > chi2   = 0.0000

                                              (Std. err. adjusted for 148 clusters in ccode)
--------------------------------------------------------------------------------------------
                           |               Robust
                milcoupsum | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------------+----------------------------------------------------------------
       1.robust_autocracy6 |   .5278635   .3128468     1.69   0.092    -.0853049    1.141032
                    troops |  -1.838259   1.027145    -1.79   0.074    -3.851427    .1749095
                           |
robust_autocracy6#c.troops |
                        1  |   2.053367   1.009086     2.03   0.042     .0755952    4.031138
                           |
            population2000 |  -2.34e-09   6.43e-09    -0.36   0.716    -1.49e-08    1.03e-08
                    gdpcap |   -.020064   .0477717    -0.42   0.674    -.1136947    .0735667
                   durable |  -.0224258   .0221588    -1.01   0.312    -.0658563    .0210046
                  conflict |   .9358275   .3751772     2.49   0.013     .2004936    1.671161
                    milexp |  -378.6024   181.1369    -2.09   0.037    -733.6242   -23.58055
                     exsol |   .9254581   2.147148     0.43   0.666    -3.282875    5.133792
                     _cons |  -2.714514   .3781303    -7.18   0.000    -3.455636   -1.973392
--------------------------------------------------------------------------------------------

. margins, dydx(robust_autocracy6) post

Average marginal effects                                 Number of obs = 3,001
Model VCE: Robust

Expression: Predicted number of events, predict()
dy/dx wrt:  1.robust_autocracy6

-------------------------------------------------------------------------------------
                    |            Delta-method
                    |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
--------------------+----------------------------------------------------------------
1.robust_autocracy6 |   .0325869   .0163034     2.00   0.046     .0006328     .064541
-------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. est store m2a

. 
. *model 2b: autocracy year fixed effects
. poisson milcoupsum robust_autocracy6##c.troops population2000 gdpcap durable conflict milexp exsol i.year, cluster(ccode)

Iteration 0:   log pseudolikelihood =   -474.314  
Iteration 1:   log pseudolikelihood = -384.68232  
Iteration 2:   log pseudolikelihood = -379.37312  
Iteration 3:   log pseudolikelihood = -377.32161  
Iteration 4:   log pseudolikelihood = -377.19739  
Iteration 5:   log pseudolikelihood = -377.19592  
Iteration 6:   log pseudolikelihood = -377.19563  
Iteration 7:   log pseudolikelihood = -377.19557  
Iteration 8:   log pseudolikelihood = -377.19555  

Poisson regression                                    Number of obs =    3,001
                                                      Wald chi2(30) = 13833.37
Log pseudolikelihood = -377.19555                     Prob > chi2   =   0.0000

                                              (Std. err. adjusted for 148 clusters in ccode)
--------------------------------------------------------------------------------------------
                           |               Robust
                milcoupsum | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------------+----------------------------------------------------------------
       1.robust_autocracy6 |   .3474264   .3130541     1.11   0.267    -.2661483    .9610011
                    troops |  -1.758985    .990541    -1.78   0.076     -3.70041    .1824396
                           |
robust_autocracy6#c.troops |
                        1  |   2.116678   1.002514     2.11   0.035     .1517879    4.081569
                           |
            population2000 |  -5.43e-09   6.96e-09    -0.78   0.435    -1.91e-08    8.21e-09
                    gdpcap |  -.0222085   .0498859    -0.45   0.656     -.119983     .075566
                   durable |  -.0226208   .0211666    -1.07   0.285    -.0641066     .018865
                  conflict |   .8681871   .3512962     2.47   0.013     .1796591    1.556715
                    milexp |  -338.2984   181.8823    -1.86   0.063    -694.7812    18.18433
                     exsol |   1.126818   1.524295     0.74   0.460    -1.860745    4.114382
                           |
                      year |
                     1992  |   -.200221   .4719885    -0.42   0.671    -1.125301    .7248595
                     1993  |  -1.129265   .6390107    -1.77   0.077    -2.381703    .1231729
                     1994  |  -.9485946   .6330356    -1.50   0.134    -2.189322    .2921324
                     1995  |  -.5200474   .4856852    -1.07   0.284    -1.471973    .4318781
                     1996  |  -.5127087   .5097367    -1.01   0.314    -1.511774    .4863568
                     1997  |  -1.493356    .583986    -2.56   0.011    -2.637948   -.3487647
                     1998  |  -2.145085    .766307    -2.80   0.005    -3.647019   -.6431505
                     1999  |  -1.181931   .5475014    -2.16   0.031    -2.255014    -.108848
                     2000  |  -1.101654   .5323014    -2.07   0.038    -2.144945   -.0583623
                     2001  |  -1.262487   .6802212    -1.86   0.063    -2.595696    .0707219
                     2002  |  -2.654042   1.065481    -2.49   0.013    -4.742347   -.5657369
                     2003  |  -1.254559   .6240738    -2.01   0.044    -2.477721   -.0313966
                     2004  |  -15.38926    .401024   -38.37   0.000    -16.17525   -14.60327
                     2005  |  -1.889177   .6057669    -3.12   0.002    -3.076458   -.7018956
                     2006  |  -1.019777    .584519    -1.74   0.081    -2.165413    .1258592
                     2007  |  -2.551027   .9035257    -2.82   0.005    -4.321904    -.780149
                     2008  |  -.8992441   .7196039    -1.25   0.211    -2.309642    .5111536
                     2009  |  -1.106929   .6741385    -1.64   0.101    -2.428216     .214358
                     2010  |  -1.008465   .7144559    -1.41   0.158    -2.408773    .3918429
                     2011  |  -2.022829   1.080325    -1.87   0.061    -4.140226    .0945692
                     2012  |  -.3212003   .6802842    -0.47   0.637    -1.654533    1.012132
                           |
                     _cons |  -1.598449   .4862834    -3.29   0.001    -2.551547   -.6453508
--------------------------------------------------------------------------------------------

. margins, dydx(robust_autocracy6) post

Average marginal effects                                 Number of obs = 3,001
Model VCE: Robust

Expression: Predicted number of events, predict()
dy/dx wrt:  1.robust_autocracy6

-------------------------------------------------------------------------------------
                    |            Delta-method
                    |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
--------------------+----------------------------------------------------------------
1.robust_autocracy6 |   .0253838   .0143517     1.77   0.077    -.0027449    .0535125
-------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. est store m2b

. 
. *model 3a: anocracy rounded
. poisson milcoupsum robust_anocracy5##c.troops_round population2000 gdpcap durable conflict milexp exsol, cluster(ccode)

Iteration 0:   log pseudolikelihood = -418.12215  
Iteration 1:   log pseudolikelihood = -411.13925  
Iteration 2:   log pseudolikelihood = -406.61847  
Iteration 3:   log pseudolikelihood = -404.13646  
Iteration 4:   log pseudolikelihood = -404.05738  
Iteration 5:   log pseudolikelihood = -404.05261  
Iteration 6:   log pseudolikelihood = -404.05162  
Iteration 7:   log pseudolikelihood =  -404.0514  
Iteration 8:   log pseudolikelihood = -404.05136  
Iteration 9:   log pseudolikelihood = -404.05135  

Poisson regression                                     Number of obs =   3,001
                                                       Wald chi2(9)  = 1573.72
Log pseudolikelihood = -404.05135                      Prob > chi2   =  0.0000

                                                   (Std. err. adjusted for 148 clusters in ccode)
-------------------------------------------------------------------------------------------------
                                |               Robust
                     milcoupsum | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------------------------+----------------------------------------------------------------
             1.robust_anocracy5 |   .6387482   .2718999     2.35   0.019     .1058341    1.171662
                   troops_round |   .0377785   .1973497     0.19   0.848    -.3490198    .4245768
                                |
robust_anocracy5#c.troops_round |
                             1  |  -14.63576   .4324955   -33.84   0.000    -15.48343   -13.78808
                                |
                 population2000 |  -8.77e-10   5.92e-09    -0.15   0.882    -1.25e-08    1.07e-08
                         gdpcap |  -.0122157   .0405784    -0.30   0.763    -.0917479    .0673165
                        durable |  -.0151555   .0220157    -0.69   0.491    -.0583055    .0279945
                       conflict |    .978658   .3516016     2.78   0.005     .2895314    1.667785
                         milexp |   -426.993   171.1355    -2.50   0.013    -762.4124   -91.57365
                          exsol |   1.085753     2.4236     0.45   0.654    -3.664415    5.835921
                          _cons |  -3.075823   .3490114    -8.81   0.000    -3.759873   -2.391774
-------------------------------------------------------------------------------------------------

. margins, dydx(robust_anocracy5) post

Average marginal effects                                 Number of obs = 3,001
Model VCE: Robust

Expression: Predicted number of events, predict()
dy/dx wrt:  1.robust_anocracy5

------------------------------------------------------------------------------------
                   |            Delta-method
                   |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------------+----------------------------------------------------------------
1.robust_anocracy5 |    .018332   .0096671     1.90   0.058    -.0006152    .0372792
------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. est store m3a

. 
. *model 3b: anocracy rounded year fixed effects
. poisson milcoupsum robust_anocracy5##c.troops_round population2000 gdpcap durable conflict milexp exsol i.year, cluster(ccode)

Iteration 0:   log pseudolikelihood = -390.90532  
Iteration 1:   log pseudolikelihood = -381.99055  
Iteration 2:   log pseudolikelihood = -377.99179  
Iteration 3:   log pseudolikelihood = -375.47997  
Iteration 4:   log pseudolikelihood = -375.36612  
Iteration 5:   log pseudolikelihood =  -375.3586  
Iteration 6:   log pseudolikelihood = -375.35788  
Iteration 7:   log pseudolikelihood = -375.35775  
Iteration 8:   log pseudolikelihood = -375.35773  

Poisson regression                                     Number of obs =   3,001
                                                       Wald chi2(30) = 9214.14
Log pseudolikelihood = -375.35773                      Prob > chi2   =  0.0000

                                                   (Std. err. adjusted for 148 clusters in ccode)
-------------------------------------------------------------------------------------------------
                                |               Robust
                     milcoupsum | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------------------------+----------------------------------------------------------------
             1.robust_anocracy5 |   .7470899   .2571726     2.91   0.004     .2430409    1.251139
                   troops_round |    .175033   .1837481     0.95   0.341    -.1851067    .5351727
                                |
robust_anocracy5#c.troops_round |
                             1  |  -14.08217   .4490068   -31.36   0.000     -14.9622   -13.20213
                                |
                 population2000 |  -4.53e-09   6.50e-09    -0.70   0.486    -1.73e-08    8.20e-09
                         gdpcap |  -.0166821   .0431143    -0.39   0.699    -.1011845    .0678203
                        durable |  -.0147026   .0199365    -0.74   0.461    -.0537773    .0243721
                       conflict |   .8240727   .3182733     2.59   0.010     .2002684    1.447877
                         milexp |  -379.8665   181.0229    -2.10   0.036    -734.6648   -25.06809
                          exsol |   1.341192   1.373755     0.98   0.329    -1.351318    4.033702
                                |
                           year |
                          1992  |  -.2404458   .4687224    -0.51   0.608    -1.159125    .6782332
                          1993  |  -1.202983    .632124    -1.90   0.057    -2.441924     .035957
                          1994  |  -1.028183   .6261878    -1.64   0.101    -2.255489    .1991222
                          1995  |  -.6447767   .4869926    -1.32   0.186    -1.599265    .3097112
                          1996  |  -.5772369   .4988756    -1.16   0.247    -1.555015    .4005414
                          1997  |  -1.543096   .5982013    -2.58   0.010    -2.715549    -.370643
                          1998  |    -2.2987    .766074    -3.00   0.003    -3.800178   -.7972231
                          1999  |  -1.365886   .5437773    -2.51   0.012     -2.43167   -.3001023
                          2000  |  -1.284511   .5321575    -2.41   0.016     -2.32752   -.2415013
                          2001  |  -1.522357   .6587473    -2.31   0.021    -2.813478   -.2312364
                          2002  |  -2.879835   1.086176    -2.65   0.008    -5.008701   -.7509698
                          2003  |  -1.485394    .615765    -2.41   0.016    -2.692271   -.2785169
                          2004  |  -16.92858   .3995094   -42.37   0.000    -17.71161   -16.14556
                          2005  |  -2.072243   .5922433    -3.50   0.000    -3.233019    -.911468
                          2006  |  -1.384871   .5938929    -2.33   0.020     -2.54888   -.2208624
                          2007  |  -2.700445   1.035827    -2.61   0.009     -4.73063    -.670261
                          2008  |  -1.044614   .6607852    -1.58   0.114    -2.339729    .2505016
                          2009  |   -1.42924   .6543668    -2.18   0.029    -2.711775   -.1467043
                          2010  |  -1.326819   .7145266    -1.86   0.063    -2.727265    .0736276
                          2011  |  -2.309665   1.090182    -2.12   0.034    -4.446383   -.1729469
                          2012  |  -.5754464   .6695996    -0.86   0.390    -1.887837    .7369447
                                |
                          _cons |  -1.864347    .418018    -4.46   0.000    -2.683647   -1.045047
-------------------------------------------------------------------------------------------------

. margins, dydx(robust_anocracy5) post

Average marginal effects                                 Number of obs = 3,001
Model VCE: Robust

Expression: Predicted number of events, predict()
dy/dx wrt:  1.robust_anocracy5

------------------------------------------------------------------------------------
                   |            Delta-method
                   |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------------+----------------------------------------------------------------
1.robust_anocracy5 |   .0223127   .0098191     2.27   0.023     .0030677    .0415577
------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. est store m3b

. 
. *model 4a: anocracy
. poisson milcoupsum robust_anocracy5##c.troops population2000 gdpcap durable conflict milexp exsol, cluster(ccode)

Iteration 0:   log pseudolikelihood = -418.40873  
Iteration 1:   log pseudolikelihood =  -412.0033  
Iteration 2:   log pseudolikelihood =   -407.598  
Iteration 3:   log pseudolikelihood = -405.16414  
Iteration 4:   log pseudolikelihood = -405.09412  
Iteration 5:   log pseudolikelihood = -405.09351  
Iteration 6:   log pseudolikelihood = -405.09351  

Poisson regression                                      Number of obs =  3,001
                                                        Wald chi2(9)  =  47.18
Log pseudolikelihood = -405.09351                       Prob > chi2   = 0.0000

                                             (Std. err. adjusted for 148 clusters in ccode)
-------------------------------------------------------------------------------------------
                          |               Robust
               milcoupsum | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------------------+----------------------------------------------------------------
       1.robust_anocracy5 |   .6782844   .2786318     2.43   0.015     .1321761    1.224393
                   troops |   .0150535   .2278477     0.07   0.947    -.4315197    .4616267
                          |
robust_anocracy5#c.troops |
                       1  |  -2.774543   .7840732    -3.54   0.000    -4.311299   -1.237788
                          |
           population2000 |  -4.52e-10   5.94e-09    -0.08   0.939    -1.21e-08    1.12e-08
                   gdpcap |  -.0128145   .0410951    -0.31   0.755    -.0933595    .0677304
                  durable |  -.0147644     .02168    -0.68   0.496    -.0572563    .0277276
                 conflict |   .9400657   .3531598     2.66   0.008     .2478853    1.632246
                   milexp |  -426.8629   172.5747    -2.47   0.013    -765.1032   -88.62272
                    exsol |   1.080705   2.444078     0.44   0.658    -3.709601     5.87101
                    _cons |  -3.068939   .3479924    -8.82   0.000    -3.750991   -2.386886
-------------------------------------------------------------------------------------------

. margins, dydx(robust_anocracy5) post

Average marginal effects                                 Number of obs = 3,001
Model VCE: Robust

Expression: Predicted number of events, predict()
dy/dx wrt:  1.robust_anocracy5

------------------------------------------------------------------------------------
                   |            Delta-method
                   |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------------+----------------------------------------------------------------
1.robust_anocracy5 |   .0183265   .0096998     1.89   0.059    -.0006847    .0373377
------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. est store m4a

. 
. *model 4b: anocracy year fixed effects
. poisson milcoupsum robust_anocracy5##c.troops population2000 gdpcap durable conflict milexp exsol i.year, cluster(ccode)

Iteration 0:   log pseudolikelihood =  -391.4553  
Iteration 1:   log pseudolikelihood = -383.10975  
Iteration 2:   log pseudolikelihood = -379.32684  
Iteration 3:   log pseudolikelihood = -376.91746  
Iteration 4:   log pseudolikelihood = -376.81701  
Iteration 5:   log pseudolikelihood = -376.81609  
Iteration 6:   log pseudolikelihood = -376.81589  
Iteration 7:   log pseudolikelihood = -376.81585  
Iteration 8:   log pseudolikelihood = -376.81584  

Poisson regression                                     Number of obs =   3,001
                                                       Wald chi2(30) = 8737.95
Log pseudolikelihood = -376.81584                      Prob > chi2   =  0.0000

                                             (Std. err. adjusted for 148 clusters in ccode)
-------------------------------------------------------------------------------------------
                          |               Robust
               milcoupsum | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------------------+----------------------------------------------------------------
       1.robust_anocracy5 |   .7817316   .2594659     3.01   0.003     .2731878    1.290275
                   troops |   .1522107   .1988262     0.77   0.444    -.2374814    .5419028
                          |
robust_anocracy5#c.troops |
                       1  |  -2.930493   .7362464    -3.98   0.000    -4.373509   -1.487476
                          |
           population2000 |  -3.97e-09   6.56e-09    -0.61   0.545    -1.68e-08    8.89e-09
                   gdpcap |  -.0167871    .043144    -0.39   0.697    -.1013479    .0677736
                  durable |  -.0144761   .0198038    -0.73   0.465    -.0532909    .0243387
                 conflict |   .7976088   .3172764     2.51   0.012     .1757584    1.419459
                   milexp |  -382.1884   181.3122    -2.11   0.035    -737.5538   -26.82308
                    exsol |   1.336649   1.394345     0.96   0.338    -1.396216    4.069515
                          |
                     year |
                    1992  |  -.2048054   .4699467    -0.44   0.663    -1.125884    .7162732
                    1993  |  -1.201046    .632525    -1.90   0.058    -2.440773    .0386797
                    1994  |  -1.010702   .6265837    -1.61   0.107    -2.238783    .2173798
                    1995  |   -.619668    .487503    -1.27   0.204    -1.575156    .3358204
                    1996  |  -.5857538   .4988413    -1.17   0.240    -1.563465    .3919571
                    1997  |  -1.544337   .5982173    -2.58   0.010    -2.716821   -.3718524
                    1998  |  -2.285881   .7647253    -2.99   0.003    -3.784715   -.7870469
                    1999  |  -1.329344   .5427933    -2.45   0.014    -2.393199   -.2654886
                    2000  |  -1.293675   .5320162    -2.43   0.015    -2.336408   -.2509427
                    2001  |  -1.513984   .6594338    -2.30   0.022    -2.806451   -.2215174
                    2002  |  -2.869951   1.085347    -2.64   0.008    -4.997193   -.7427091
                    2003  |  -1.452439   .6180022    -2.35   0.019    -2.663701   -.2411768
                    2004  |  -16.12357   .3960668   -40.71   0.000    -16.89985    -15.3473
                    2005  |  -2.037049   .5980494    -3.41   0.001    -3.209204   -.8648937
                    2006  |  -1.325479   .5988226    -2.21   0.027     -2.49915   -.1518085
                    2007  |  -2.635413   1.033337    -2.55   0.011    -4.660717   -.6101091
                    2008  |  -.9908033   .6588186    -1.50   0.133    -2.282064    .3004574
                    2009  |  -1.369943   .6596404    -2.08   0.038    -2.662814   -.0770711
                    2010  |  -1.294938   .7163836    -1.81   0.071    -2.699024    .1091481
                    2011  |  -2.279744    1.09334    -2.09   0.037    -4.422651   -.1368382
                    2012  |  -.5448664   .6740059    -0.81   0.419    -1.865894     .776161
                          |
                    _cons |  -1.883731   .4172994    -4.51   0.000    -2.701623   -1.065839
-------------------------------------------------------------------------------------------

. margins, dydx(robust_anocracy5) post

Average marginal effects                                 Number of obs = 3,001
Model VCE: Robust

Expression: Predicted number of events, predict()
dy/dx wrt:  1.robust_anocracy5

------------------------------------------------------------------------------------
                   |            Delta-method
                   |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------------+----------------------------------------------------------------
1.robust_anocracy5 |   .0223201   .0097412     2.29   0.022     .0032278    .0414124
------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. est store m4b

. 
. *model 5a: democracy rounded
. eststo: poisson milcoupsum robust_democracy6##c.troops_round population2000 gdpcap durable conflict milexp exsol, cluster(ccode)

Iteration 0:   log pseudolikelihood = -408.65114  
Iteration 1:   log pseudolikelihood = -403.73711  
Iteration 2:   log pseudolikelihood =  -400.3097  
Iteration 3:   log pseudolikelihood = -397.87753  
Iteration 4:   log pseudolikelihood = -397.85044  
Iteration 5:   log pseudolikelihood = -397.85041  

Poisson regression                                      Number of obs =  3,001
                                                        Wald chi2(9)  = 106.76
Log pseudolikelihood = -397.85041                       Prob > chi2   = 0.0000

                                                    (Std. err. adjusted for 148 clusters in ccode)
--------------------------------------------------------------------------------------------------
                                 |               Robust
                      milcoupsum | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------------------+----------------------------------------------------------------
             1.robust_democracy6 |  -1.266283   .3889775    -3.26   0.001    -2.028665   -.5039008
                    troops_round |  -.1877026   .3618988    -0.52   0.604    -.8970113    .5216061
                                 |
robust_democracy6#c.troops_round |
                              1  |  -.5632723   .3902558    -1.44   0.149     -1.32816    .2016151
                                 |
                  population2000 |  -6.38e-10   6.11e-09    -0.10   0.917    -1.26e-08    1.13e-08
                          gdpcap |  -.0056696   .0292482    -0.19   0.846    -.0629949    .0516558
                         durable |  -.0188081   .0218902    -0.86   0.390    -.0617121    .0240959
                        conflict |   .8063916    .339749     2.37   0.018     .1404957    1.472287
                          milexp |   -382.345   162.0019    -2.36   0.018    -699.8628   -64.82717
                           exsol |   .6583073   2.288884     0.29   0.774    -3.827822    5.144437
                           _cons |  -2.371782   .3216645    -7.37   0.000    -3.002233   -1.741331
--------------------------------------------------------------------------------------------------
(est1 stored)

. margins, dydx(robust_democracy6) post

Average marginal effects                                 Number of obs = 3,001
Model VCE: Robust

Expression: Predicted number of events, predict()
dy/dx wrt:  1.robust_democracy6

-------------------------------------------------------------------------------------
                    |            Delta-method
                    |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
--------------------+----------------------------------------------------------------
1.robust_democracy6 |  -.0369207   .0088655    -4.16   0.000    -.0542967   -.0195447
-------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. est store m5a

. 
. *model 5b: democracy rounded year fixed effects
. eststo: poisson milcoupsum robust_democracy6##c.troops_round population2000 gdpcap durable conflict milexp exsol i.year, cluster(ccode)

Iteration 0:   log pseudolikelihood = -385.27656  
Iteration 1:   log pseudolikelihood = -378.41499  
Iteration 2:   log pseudolikelihood = -375.15773  
Iteration 3:   log pseudolikelihood = -373.05016  
Iteration 4:   log pseudolikelihood = -372.98777  
Iteration 5:   log pseudolikelihood =  -372.9869  
Iteration 6:   log pseudolikelihood = -372.98672  
Iteration 7:   log pseudolikelihood = -372.98667  
Iteration 8:   log pseudolikelihood = -372.98667  

Poisson regression                                     Number of obs =   3,001
                                                       Wald chi2(30) = 9460.20
Log pseudolikelihood = -372.98667                      Prob > chi2   =  0.0000

                                                    (Std. err. adjusted for 148 clusters in ccode)
--------------------------------------------------------------------------------------------------
                                 |               Robust
                      milcoupsum | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------------------+----------------------------------------------------------------
             1.robust_democracy6 |  -1.244421   .3774961    -3.30   0.001      -1.9843    -.504542
                    troops_round |  -.1572293   .3852716    -0.41   0.683    -.9123477    .5978891
                                 |
robust_democracy6#c.troops_round |
                              1  |   -.513842   .3703016    -1.39   0.165     -1.23962    .2119359
                                 |
                  population2000 |  -3.11e-09   6.53e-09    -0.48   0.635    -1.59e-08    9.70e-09
                          gdpcap |  -.0076993   .0297279    -0.26   0.796    -.0659649    .0505664
                         durable |  -.0205507    .020731    -0.99   0.322    -.0611826    .0200813
                        conflict |   .7104929   .3140018     2.26   0.024     .0950607    1.325925
                          milexp |  -348.5334   165.5314    -2.11   0.035    -672.9691    -24.0978
                           exsol |   .8386633   1.537981     0.55   0.586    -2.175724    3.853051
                                 |
                            year |
                           1992  |  -.1845682   .4660387    -0.40   0.692    -1.097987    .7288508
                           1993  |  -1.110312   .6352628    -1.75   0.080    -2.355404    .1347805
                           1994  |  -.8929824   .6205498    -1.44   0.150    -2.109238    .3232728
                           1995  |  -.5476994   .4722493    -1.16   0.246    -1.473291    .3778923
                           1996  |  -.5247924   .4962045    -1.06   0.290    -1.497335    .4477506
                           1997  |  -1.488066   .5771979    -2.58   0.010    -2.619354   -.3567793
                           1998  |  -2.190873    .762715    -2.87   0.004    -3.685767   -.6959789
                           1999  |  -1.204569   .5349161    -2.25   0.024    -2.252985   -.1561525
                           2000  |  -1.153537   .5207769    -2.22   0.027    -2.174241    -.132833
                           2001  |  -1.315381   .6756005    -1.95   0.052    -2.639534    .0087717
                           2002  |  -2.677537   1.067006    -2.51   0.012     -4.76883   -.5862443
                           2003  |  -1.254087   .6114941    -2.05   0.040    -2.452593   -.0555805
                           2004  |  -16.47522   .3856727   -42.72   0.000    -17.23112   -15.71931
                           2005  |  -1.754576   .5891561    -2.98   0.003    -2.909301   -.5998511
                           2006  |  -1.016695   .5786619    -1.76   0.079    -2.150852    .1174615
                           2007  |  -2.341795   1.075514    -2.18   0.029    -4.449763   -.2338268
                           2008  |   -.669245    .651054    -1.03   0.304    -1.945288    .6067974
                           2009  |  -1.086134   .6567008    -1.65   0.098    -2.373244    .2009764
                           2010  |  -.9796571   .6976452    -1.40   0.160    -2.347017    .3877024
                           2011  |  -1.971761   1.096499    -1.80   0.072     -4.12086    .1773379
                           2012  |   -.269943   .6747632    -0.40   0.689    -1.592455    1.052569
                                 |
                           _cons |  -1.295856   .4263056    -3.04   0.002      -2.1314   -.4603127
--------------------------------------------------------------------------------------------------
(est2 stored)

. margins, dydx(robust_democracy6) post

Average marginal effects                                 Number of obs = 3,001
Model VCE: Robust

Expression: Predicted number of events, predict()
dy/dx wrt:  1.robust_democracy6

-------------------------------------------------------------------------------------
                    |            Delta-method
                    |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
--------------------+----------------------------------------------------------------
1.robust_democracy6 |  -.0361343   .0087589    -4.13   0.000    -.0533014   -.0189672
-------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. est store m5b

. 
. *model 6a: democracy
. eststo: poisson milcoupsum robust_democracy6##c.troops population2000 gdpcap durable conflict milexp exsol, cluster(ccode)

Iteration 0:   log pseudolikelihood = -408.62583  
Iteration 1:   log pseudolikelihood = -403.75464  
Iteration 2:   log pseudolikelihood = -400.33051  
Iteration 3:   log pseudolikelihood = -397.91594  
Iteration 4:   log pseudolikelihood = -397.88968  
Iteration 5:   log pseudolikelihood = -397.88965  

Poisson regression                                      Number of obs =  3,001
                                                        Wald chi2(9)  =  80.92
Log pseudolikelihood = -397.88965                       Prob > chi2   = 0.0000

                                              (Std. err. adjusted for 148 clusters in ccode)
--------------------------------------------------------------------------------------------
                           |               Robust
                milcoupsum | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------------+----------------------------------------------------------------
       1.robust_democracy6 |  -1.249778   .3992128    -3.13   0.002    -2.032221   -.4673356
                    troops |  -.1917374   .3705899    -0.52   0.605    -.9180804    .5346055
                           |
robust_democracy6#c.troops |
                        1  |   -.564566   .6630028    -0.85   0.394    -1.864028    .7348957
                           |
            population2000 |  -5.57e-10   6.02e-09    -0.09   0.926    -1.23e-08    1.12e-08
                    gdpcap |  -.0058757    .029398    -0.20   0.842    -.0634948    .0517433
                   durable |   -.018721   .0217927    -0.86   0.390    -.0614339     .023992
                  conflict |   .8028679   .3393845     2.37   0.018     .1376865    1.468049
                    milexp |  -380.7427     162.99    -2.34   0.019    -700.1972   -61.28813
                     exsol |   .6661069   2.266833     0.29   0.769    -3.776804    5.109018
                     _cons |  -2.370419   .3210252    -7.38   0.000    -2.999617   -1.741222
--------------------------------------------------------------------------------------------
(est3 stored)

. margins, dydx(robust_democracy6) post

Average marginal effects                                 Number of obs = 3,001
Model VCE: Robust

Expression: Predicted number of events, predict()
dy/dx wrt:  1.robust_democracy6

-------------------------------------------------------------------------------------
                    |            Delta-method
                    |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
--------------------+----------------------------------------------------------------
1.robust_democracy6 |  -.0367949   .0088926    -4.14   0.000     -.054224   -.0193659
-------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. est store m6a

. 
. *model 6b: democracy year fixed effects
. eststo: poisson milcoupsum robust_democracy6##c.troops population2000 gdpcap durable conflict milexp exsol i.year, cluster(ccode)

Iteration 0:   log pseudolikelihood = -385.30408  
Iteration 1:   log pseudolikelihood = -378.49581  
Iteration 2:   log pseudolikelihood = -375.24587  
Iteration 3:   log pseudolikelihood = -373.13801  
Iteration 4:   log pseudolikelihood = -373.07491  
Iteration 5:   log pseudolikelihood = -373.07404  
Iteration 6:   log pseudolikelihood = -373.07385  
Iteration 7:   log pseudolikelihood = -373.07381  
Iteration 8:   log pseudolikelihood =  -373.0738  

Poisson regression                                     Number of obs =   3,001
                                                       Wald chi2(30) = 8652.34
Log pseudolikelihood = -373.0738                       Prob > chi2   =  0.0000

                                              (Std. err. adjusted for 148 clusters in ccode)
--------------------------------------------------------------------------------------------
                           |               Robust
                milcoupsum | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------------+----------------------------------------------------------------
       1.robust_democracy6 |  -1.233484   .3848285    -3.21   0.001    -1.987735   -.4792344
                    troops |  -.1543723   .3870665    -0.40   0.690    -.9130086     .604264
                           |
robust_democracy6#c.troops |
                        1  |  -.4891277   .5904536    -0.83   0.407    -1.646395    .6681401
                           |
            population2000 |  -3.04e-09   6.42e-09    -0.47   0.636    -1.56e-08    9.55e-09
                    gdpcap |  -.0078074   .0298136    -0.26   0.793    -.0662411    .0506263
                   durable |  -.0205296   .0206891    -0.99   0.321    -.0610794    .0200202
                  conflict |   .7093504   .3137122     2.26   0.024     .0944858    1.324215
                    milexp |  -348.2272   166.2554    -2.09   0.036    -674.0819   -22.37258
                     exsol |   .8459835   1.533316     0.55   0.581     -2.15926    3.851227
                           |
                      year |
                     1992  |  -.1799123    .466459    -0.39   0.700    -1.094155    .7343306
                     1993  |  -1.111726   .6362058    -1.75   0.081    -2.358667     .135214
                     1994  |  -.8926369   .6220897    -1.43   0.151     -2.11191    .3266364
                     1995  |   -.542125   .4732209    -1.15   0.252    -1.469621    .3853709
                     1996  |  -.5253178   .4966491    -1.06   0.290    -1.498732    .4480966
                     1997  |  -1.487289   .5774415    -2.58   0.010    -2.619053   -.3555244
                     1998  |  -2.190386   .7630204    -2.87   0.004    -3.685879   -.6948938
                     1999  |  -1.203542    .535083    -2.25   0.024    -2.252285   -.1547987
                     2000  |  -1.157645     .52102    -2.22   0.026    -2.178826   -.1364646
                     2001  |  -1.313838   .6760268    -1.94   0.052    -2.638826    .0111504
                     2002  |  -2.675284   1.068164    -2.50   0.012    -4.768848   -.5817205
                     2003  |  -1.252785   .6125597    -2.05   0.041     -2.45338   -.0521902
                     2004  |    -15.717   .3877856   -40.53   0.000    -16.47704   -14.95695
                     2005  |  -1.746964   .5900215    -2.96   0.003    -2.903385   -.5905432
                     2006  |  -1.007078   .5809118    -1.73   0.083    -2.145644    .1314882
                     2007  |  -2.328517   1.051082    -2.22   0.027    -4.388601   -.2684335
                     2008  |  -.6570954     .65678    -1.00   0.317    -1.944361    .6301698
                     2009  |  -1.074256   .6636811    -1.62   0.106    -2.375047    .2265348
                     2010  |  -.9739916   .7028171    -1.39   0.166    -2.351488    .4035047
                     2011  |  -1.970144   1.101736    -1.79   0.074    -4.129507    .1892201
                     2012  |  -.2671558   .6811879    -0.39   0.695     -1.60226    1.067948
                           |
                     _cons |  -1.298313   .4248104    -3.06   0.002    -2.130926   -.4656996
--------------------------------------------------------------------------------------------
(est4 stored)

. margins, dydx(robust_democracy6) post

Average marginal effects                                 Number of obs = 3,001
Model VCE: Robust

Expression: Predicted number of events, predict()
dy/dx wrt:  1.robust_democracy6

-------------------------------------------------------------------------------------
                    |            Delta-method
                    |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
--------------------+----------------------------------------------------------------
1.robust_democracy6 |  -.0360587   .0087608    -4.12   0.000    -.0532296   -.0188879
-------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. est store m6b

. 
. *model 7a: New democracy rounded
. eststo: poisson milcoupsum new_robust_democracy6##c.troops_round population2000 gdpcap durable conflict milexp exsol, cluster(ccode)

Iteration 0:   log pseudolikelihood = -411.80855  
Iteration 1:   log pseudolikelihood = -406.88091  
Iteration 2:   log pseudolikelihood = -403.34873  
Iteration 3:   log pseudolikelihood = -400.71097  
Iteration 4:   log pseudolikelihood = -400.68018  
Iteration 5:   log pseudolikelihood = -400.68014  

Poisson regression                                      Number of obs =  3,001
                                                        Wald chi2(9)  =  98.89
Log pseudolikelihood = -400.68014                       Prob > chi2   = 0.0000

                                                        (Std. err. adjusted for 148 clusters in ccode)
------------------------------------------------------------------------------------------------------
                                     |               Robust
                          milcoupsum | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------------------------------+----------------------------------------------------------------
             1.new_robust_democracy6 |    -1.1412   .3973375    -2.87   0.004    -1.919967   -.3624327
                        troops_round |  -.1700076   .3497482    -0.49   0.627    -.8555015    .5154863
                                     |
new_robust_democracy6#c.troops_round |
                                  1  |   -.627903   .4140957    -1.52   0.129    -1.439516    .1837097
                                     |
                      population2000 |  -4.10e-10   6.15e-09    -0.07   0.947    -1.25e-08    1.16e-08
                              gdpcap |  -.0073794   .0312009    -0.24   0.813    -.0685321    .0537733
                             durable |  -.0168181   .0213251    -0.79   0.430    -.0586145    .0249782
                            conflict |     .81099    .345018     2.35   0.019     .1347672    1.487213
                              milexp |  -400.6625   169.9969    -2.36   0.018    -733.8504   -67.47471
                               exsol |   .7672314   2.145492     0.36   0.721    -3.437857    4.972319
                               _cons |  -2.430964   .3200902    -7.59   0.000     -3.05833   -1.803599
------------------------------------------------------------------------------------------------------
(est5 stored)

. margins, dydx(new_robust_democracy6) post

Average marginal effects                                 Number of obs = 3,001
Model VCE: Robust

Expression: Predicted number of events, predict()
dy/dx wrt:  1.new_robust_democracy6

-----------------------------------------------------------------------------------------
                        |            Delta-method
                        |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
------------------------+----------------------------------------------------------------
1.new_robust_democracy6 |   -.033565   .0089892    -3.73   0.000    -.0511836   -.0159465
-----------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. est store m7a

. 
. *model 7b: New democracy rounded year fixed effects
. eststo: poisson milcoupsum new_robust_democracy6##c.troops_round population2000 gdpcap durable conflict milexp exsol i.year, cluster(ccode)

Iteration 0:   log pseudolikelihood = -387.69383  
Iteration 1:   log pseudolikelihood = -380.98709  
Iteration 2:   log pseudolikelihood =  -377.6916  
Iteration 3:   log pseudolikelihood = -375.45558  
Iteration 4:   log pseudolikelihood = -375.39277  
Iteration 5:   log pseudolikelihood = -375.39189  
Iteration 6:   log pseudolikelihood = -375.39171  
Iteration 7:   log pseudolikelihood = -375.39166  
Iteration 8:   log pseudolikelihood = -375.39166  

Poisson regression                                     Number of obs =   3,001
                                                       Wald chi2(30) = 9502.65
Log pseudolikelihood = -375.39166                      Prob > chi2   =  0.0000

                                                        (Std. err. adjusted for 148 clusters in ccode)
------------------------------------------------------------------------------------------------------
                                     |               Robust
                          milcoupsum | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------------------------------+----------------------------------------------------------------
             1.new_robust_democracy6 |  -1.123263   .3846878    -2.92   0.004    -1.877237   -.3692884
                        troops_round |  -.1348261   .3759408    -0.36   0.720    -.8716565    .6020043
                                     |
new_robust_democracy6#c.troops_round |
                                  1  |   -.565387   .3921266    -1.44   0.149    -1.333941     .203167
                                     |
                      population2000 |  -2.97e-09   6.50e-09    -0.46   0.648    -1.57e-08    9.78e-09
                              gdpcap |   -.009593    .032033    -0.30   0.765    -.0723765    .0531906
                             durable |  -.0185234   .0199177    -0.93   0.352    -.0575613    .0205146
                            conflict |   .7109894     .31929     2.23   0.026     .0851925    1.336786
                              milexp |  -361.6568   174.9984    -2.07   0.039    -704.6474   -18.66617
                               exsol |   .9384113    1.44349     0.65   0.516    -1.890776    3.767599
                                     |
                                year |
                               1992  |  -.1898623    .465948    -0.41   0.684    -1.103104     .723379
                               1993  |  -1.113529   .6360302    -1.75   0.080    -2.360125    .1330674
                               1994  |  -.8960356   .6220551    -1.44   0.150    -2.115241      .32317
                               1995  |  -.5461809   .4730008    -1.15   0.248    -1.473245    .3808837
                               1996  |  -.5203781   .4972369    -1.05   0.295    -1.494945    .4541882
                               1997  |  -1.483159   .5782151    -2.57   0.010    -2.616439   -.3498779
                               1998  |  -2.185861   .7633821    -2.86   0.004    -3.682063   -.6896601
                               1999  |  -1.200096   .5352299    -2.24   0.025    -2.249127   -.1510644
                               2000  |  -1.154248   .5205723    -2.22   0.027    -2.174551   -.1339447
                               2001  |  -1.340416   .6768569    -1.98   0.048    -2.667032   -.0138012
                               2002  |  -2.690437   1.067305    -2.52   0.012    -4.782317   -.5985573
                               2003  |  -1.291521   .6112331    -2.11   0.035    -2.489516   -.0935262
                               2004  |  -16.51315   .3816488   -43.27   0.000    -17.26117   -15.76514
                               2005  |  -1.812349   .5912783    -3.07   0.002    -2.971234   -.6534653
                               2006  |   -1.07838   .5778618    -1.87   0.062    -2.210968    .0542087
                               2007  |  -2.405837   1.075299    -2.24   0.025    -4.513384   -.2982899
                               2008  |   -.741657   .6471497    -1.15   0.252    -2.010047     .526733
                               2009  |  -1.164786   .6571383    -1.77   0.076    -2.452754     .123181
                               2010  |  -1.048219   .6942592    -1.51   0.131    -2.408942     .312504
                               2011  |  -2.066684   1.089716    -1.90   0.058    -4.202489    .0691201
                               2012  |   -.375644   .6740534    -0.56   0.577    -1.696764    .9454764
                                     |
                               _cons |  -1.330081   .4272726    -3.11   0.002     -2.16752   -.4926421
------------------------------------------------------------------------------------------------------
(est6 stored)

. margins, dydx(new_robust_democracy6) post

Average marginal effects                                 Number of obs = 3,001
Model VCE: Robust

Expression: Predicted number of events, predict()
dy/dx wrt:  1.new_robust_democracy6

-----------------------------------------------------------------------------------------
                        |            Delta-method
                        |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
------------------------+----------------------------------------------------------------
1.new_robust_democracy6 |  -.0328777   .0087825    -3.74   0.000    -.0500911   -.0156643
-----------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. est store m7b

. 
. *model 8a: New democracy
. eststo: poisson milcoupsum new_robust_democracy6##c.troops population2000 gdpcap durable conflict milexp exsol, cluster(ccode)

Iteration 0:   log pseudolikelihood = -411.78171  
Iteration 1:   log pseudolikelihood = -406.87226  
Iteration 2:   log pseudolikelihood = -403.34415  
Iteration 3:   log pseudolikelihood = -400.72867  
Iteration 4:   log pseudolikelihood = -400.69906  
Iteration 5:   log pseudolikelihood = -400.69903  

Poisson regression                                      Number of obs =  3,001
                                                        Wald chi2(9)  =  72.73
Log pseudolikelihood = -400.69903                       Prob > chi2   = 0.0000

                                                  (Std. err. adjusted for 148 clusters in ccode)
------------------------------------------------------------------------------------------------
                               |               Robust
                    milcoupsum | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------------------------+----------------------------------------------------------------
       1.new_robust_democracy6 |  -1.118677   .4094854    -2.73   0.006    -1.921254   -.3161008
                        troops |  -.1720984   .3543506    -0.49   0.627    -.8666128    .5224159
                               |
new_robust_democracy6#c.troops |
                            1  |  -.6602458   .7434278    -0.89   0.374    -2.117338     .796846
                               |
                population2000 |  -3.33e-10   6.06e-09    -0.05   0.956    -1.22e-08    1.15e-08
                        gdpcap |   -.007618   .0313899    -0.24   0.808    -.0691412    .0539051
                       durable |  -.0167517    .021227    -0.79   0.430    -.0583558    .0248523
                      conflict |   .8082747   .3447763     2.34   0.019     .1325255    1.484024
                        milexp |  -398.4421   171.2426    -2.33   0.020    -734.0715   -62.81271
                         exsol |   .7746237   2.123611     0.36   0.715    -3.387578    4.936825
                         _cons |  -2.430278    .319332    -7.61   0.000    -3.056157   -1.804399
------------------------------------------------------------------------------------------------
(est7 stored)

. margins, dydx(new_robust_democracy6) post

Average marginal effects                                 Number of obs = 3,001
Model VCE: Robust

Expression: Predicted number of events, predict()
dy/dx wrt:  1.new_robust_democracy6

-----------------------------------------------------------------------------------------
                        |            Delta-method
                        |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
------------------------+----------------------------------------------------------------
1.new_robust_democracy6 |  -.0333935    .009051    -3.69   0.000    -.0511331   -.0156538
-----------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. est store m8a

. 
. *model 8b: New democracy year fixed effects
. eststo: poisson milcoupsum new_robust_democracy6##c.troops population2000 gdpcap durable conflict milexp exsol i.year, cluster(ccode)

Iteration 0:   log pseudolikelihood = -387.72275  
Iteration 1:   log pseudolikelihood = -381.05754  
Iteration 2:   log pseudolikelihood = -377.77238  
Iteration 3:   log pseudolikelihood = -375.53793  
Iteration 4:   log pseudolikelihood = -375.47456  
Iteration 5:   log pseudolikelihood = -375.47367  
Iteration 6:   log pseudolikelihood = -375.47348  
Iteration 7:   log pseudolikelihood = -375.47344  
Iteration 8:   log pseudolikelihood = -375.47343  

Poisson regression                                     Number of obs =   3,001
                                                       Wald chi2(30) = 8686.11
Log pseudolikelihood = -375.47343                      Prob > chi2   =  0.0000

                                                  (Std. err. adjusted for 148 clusters in ccode)
------------------------------------------------------------------------------------------------
                               |               Robust
                    milcoupsum | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------------------------+----------------------------------------------------------------
       1.new_robust_democracy6 |  -1.108746   .3937836    -2.82   0.005    -1.880548   -.3369445
                        troops |  -.1288217   .3725482    -0.35   0.730    -.8590028    .6013595
                               |
new_robust_democracy6#c.troops |
                            1  |  -.5571352   .6505744    -0.86   0.392    -1.832238    .7179673
                               |
                population2000 |  -2.92e-09   6.40e-09    -0.46   0.648    -1.55e-08    9.62e-09
                        gdpcap |  -.0097187   .0321368    -0.30   0.762    -.0727057    .0532682
                       durable |  -.0185098   .0198802    -0.93   0.352    -.0574743    .0204546
                      conflict |   .7104872     .31907     2.23   0.026     .0851215    1.335853
                        milexp |  -361.0201    175.802    -2.05   0.040    -705.5857   -16.45451
                         exsol |   .9453657   1.438614     0.66   0.511    -1.874267    3.764998
                               |
                          year |
                         1992  |  -.1858376   .4663328    -0.40   0.690    -1.099833     .728158
                         1993  |  -1.115243   .6369458    -1.75   0.080    -2.363634     .133148
                         1994  |  -.8965117   .6235488    -1.44   0.151    -2.118645    .3256214
                         1995  |  -.5412889   .4738989    -1.14   0.253    -1.470114    .3875358
                         1996  |  -.5206751   .4976592    -1.05   0.295    -1.496069     .454719
                         1997  |  -1.482248   .5784632    -2.56   0.010    -2.616015   -.3484807
                         1998  |  -2.185406   .7637233    -2.86   0.004    -3.682276   -.6885362
                         1999  |  -1.199833   .5354157    -2.24   0.025    -2.249228   -.1504372
                         2000  |  -1.158351   .5208029    -2.22   0.026    -2.179106   -.1375955
                         2001  |  -1.338814   .6771997    -1.98   0.048    -2.666101    -.011527
                         2002  |   -2.68816    1.06862    -2.52   0.012    -4.782618   -.5937028
                         2003  |  -1.290712   .6123261    -2.11   0.035    -2.490849   -.0905752
                         2004  |  -15.75532   .3837379   -41.06   0.000    -16.50743   -15.00321
                         2005  |  -1.804979   .5920026    -3.05   0.002    -2.965282   -.6446749
                         2006  |  -1.069061   .5801126    -1.84   0.065    -2.206061    .0679388
                         2007  |  -2.392854   1.050674    -2.28   0.023    -4.452137    -.333571
                         2008  |  -.7299687   .6533301    -1.12   0.264    -2.010472    .5505347
                         2009  |  -1.154156   .6639111    -1.74   0.082    -2.455398    .1470857
                         2010  |  -1.043341   .6998146    -1.49   0.136    -2.414952    .3282704
                         2011  |  -2.065577   1.095305    -1.89   0.059    -4.212336    .0811818
                         2012  |  -.3733699   .6803906    -0.55   0.583    -1.706911    .9601711
                               |
                         _cons |  -1.332866   .4257707    -3.13   0.002    -2.167361   -.4983708
------------------------------------------------------------------------------------------------
(est8 stored)

. margins, dydx(new_robust_democracy6) post

Average marginal effects                                 Number of obs = 3,001
Model VCE: Robust

Expression: Predicted number of events, predict()
dy/dx wrt:  1.new_robust_democracy6

-----------------------------------------------------------------------------------------
                        |            Delta-method
                        |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
------------------------+----------------------------------------------------------------
1.new_robust_democracy6 |   -.032773    .008807    -3.72   0.000    -.0500344   -.0155115
-----------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. est store m8b

. 
. *coefplot
. coefplot (m1a, label(Autocracy * Rounded Peacekeepers) offset(0.1)) ///
> (m2a, label(Autocracy) offset(-0.1)) ///
> (m3a, label(Anocracy * Rounded Peacekeepers) offset(0.1)) ///
> (m4a, label(Anocracy) offset(-0.1)) ///
> (m5a, label(Democracy * Rounded Peacekeepers) offset(0.1)) ///
> (m6a, label(Democracy) offset(-0.1)) ///
> (m7a, label(New Democracy * Rounded Peacekeepers) offset(0.1)) ///
> (m8a, label(New Democracy) offset(-0.1)), xline(0) ///
> title("", size(medsmall) span) xtitle("Average marginal effects", size(small)) ylabel(,labsize(vsmall)) xlabel(,labsize(vsmall)) legend(size(vsmall) span)

. 
. graph export "figures\Study_2\Figure_B1_without_year_fe.png", replace
file figures\Study_2\Figure_B1_without_year_fe.png saved as PNG format

. 
. 
. *coefplot
. coefplot (m1b, label(Autocracy * Rounded Peacekeepers) offset(0.1))  ///
> (m2b, label(Autocracy) offset(-0.1)) ///
> (m3b, label(Anocracy * Rounded Peacekeepers) offset(0.1)) ///
> (m4b, label(Anocracy) offset(-0.1)) ///
> (m5b, label(Democracy * Rounded Peacekeepers) offset(0.1)) ///
> (m6b, label(Democracy) offset(-0.1)) ///
> (m7b, label(New Democracy * Rounded Peacekeepers) offset(0.1)) ///
> (m8b, label(New Democracy) offset(-0.1)), xline(0) ///
> title("", size(medsmall) span) xtitle("Average marginal effects", size(small)) ylabel(,labsize(vsmall)) xlabel(,labsize(vsmall)) legend(size(vsmall) span)

. 
. graph export "figures\Study_2\Figure_B2_with_year_fe.png", replace
file figures\Study_2\Figure_B2_with_year_fe.png saved as PNG format

. 
end of do-file

. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. 
. *------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------*
. * Figure 2 Main Paper
. *------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------*
. 
. *** Loading dataset
. use "data\Study_2\original_data.dta", clear //opening replication dataset

. describe

Contains data from data\Study_2\original_data.dta
 Observations:         4,740                  
    Variables:            61                  17 Nov 2019 08:11
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Variable      Storage   Display    Value
    name         type    format    label      Variable label
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
year            int     %8.0g                 
ccode           float   %9.0g                 
marshallsum     byte    %8.0g                 
milcoupsum      byte    %8.0g                 
milcoupsucces~m byte    %8.0g                 
troops          float   %9.0g                 
milexp          float   %9.0g                 
exsol           float   %9.0g                 
polity2         byte    %8.0g                 
durable         int     %8.0g                 
conflict        byte    %8.0g                 
gdpcap          float   %9.0g                 
chgdppc_l       float   %9.0g                 
elf             float   %9.0g                 
effective_num~r float   %9.0g                 
aggdemand       float   %9.0g                 
pko100          byte    %8.0g                 
pko100lag       byte    %8.0g                 
pko500          byte    %8.0g                 
pko500lag       byte    %8.0g                 
polity2lag      byte    %8.0g                 
conflictlag     byte    %8.0g                 
durablelag      int     %8.0g                 
population2000  long    %12.0g                
continent       byte    %8.0g                 
milcoup         byte    %8.0g                 
coupmarshall3   byte    %8.0g                 
coupmarshall3~g byte    %8.0g                 
ln_exsol        float   %9.0g                 
coupsum_mil_p~l byte    %8.0g                 
milcoupsucces~1 byte    %8.0g                 milcoupsuccess_mm.1
twothousands    byte    %8.0g                 
milcoupsucces~2 byte    %8.0g                 
milcoupfailed~2 byte    %8.0g                 
milcouplag2     byte    %8.0g                 
troopslag2      float   %9.0g                 
polity2lag2     byte    %8.0g                 
milcouplag      byte    %8.0g                 
robust_democr~3 float   %9.0g                 
robust_democr~4 float   %9.0g                 
robust_democr~5 float   %9.0g                 
robust_democr~6 float   %9.0g                 
robust_democr~7 float   %9.0g                 
robust_autocr~3 float   %9.0g                 
robust_autocr~4 float   %9.0g                 
robust_autocr~5 float   %9.0g                 
robust_autocr~6 float   %9.0g                 
robust_autocr~7 float   %9.0g                 
robust_anocra~3 float   %9.0g                 
robust_anocra~4 float   %9.0g                 
robust_anocra~5 float   %9.0g                 
robust_anocra~6 float   %9.0g                 
robust_anocra~2 float   %9.0g                 
robust_anocra~1 float   %9.0g                 
aut_troops      float   %9.0g                 
ano_troops      float   %9.0g                 
dem_troops      float   %9.0g                 
aut_troops_ro~d float   %9.0g                 
ano_troops_ro~d float   %9.0g                 
dem_troops_ro~d float   %9.0g                 
milcoupsum_cu~e float   %9.0g                 
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Sorted by: ccode  year

. 
. *------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------*
. * Generating variables
. *------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------*
. **generating New robust democracy dummy variable**
. gen new_robust_democracy6 = . //generating a new variable for the fixed version of robust_democracy6
(4,740 missing values generated)

. replace new_robust_democracy6 = robust_democracy6
(4,740 real changes made)

. replace new_robust_democracy6 = 0 if polity2 == . //missings von polity2 als new_robust_autocracy6 == 0 codiert 
(1,214 real changes made)

. 
. 
. *------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------*
. * Labeling of varlabels
. *------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------*
. label variable marshallsum "All coup attempts"

. label variable milcoupsum "Military coup attempts"

. label variable milcoupsuccess_mm "Successful military coups"

. label variable troops "Peacekeepers"

. label variable robust_autocracy6 "Robust autocracy"

. label variable robust_anocracy5 "Anocracy"

. label variable robust_democracy6 "Robust democracy"

. label variable population2000 "Population"

. label variable gdpcap "GDP per capita"

. label variable durable "Regime durability"

. label variable conflict "Political violence"

. label variable milexp "Military expenditure"

. label variable exsol "Expenditure per soldier"

. label variable effective_number "Effective organizations"

. label variable elf "Ethnic fractionalization"

. label variable aut_troops "Autocracy Peacekeepers"

. label variable ano_troops "Anocracy Peacekeepers"

. label variable dem_troops "Democracy Peacekeepers"

. label variable aut_troops_round "Autocracy Peacekeepers rounded"

. label variable ano_troops_round "Anocracy Peacekeepers rounded"

. label variable dem_troops_round "Democracy Peacekeepers rounded"

. label variable new_robust_democracy6 "New robust democracy"

. 
end of do-file

. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

.            
. **FIGURE 1**
. **model 1: autocracy
. poisson milcoupsum robust_autocracy6 c.troops aut_troops_round population2000 gdpcap durable conflict milexp exsol, cluster(ccode)

Iteration 0:   log pseudolikelihood = -459.40498  
Iteration 1:   log pseudolikelihood = -409.67311  
Iteration 2:   log pseudolikelihood = -403.36282  
Iteration 3:   log pseudolikelihood = -401.41824  
Iteration 4:   log pseudolikelihood =  -401.3271  
Iteration 5:   log pseudolikelihood = -401.32654  
Iteration 6:   log pseudolikelihood = -401.32654  

Poisson regression                                      Number of obs =  3,001
                                                        Wald chi2(9)  =  82.52
Log pseudolikelihood = -401.32654                       Prob > chi2   = 0.0000

                                     (Std. err. adjusted for 148 clusters in ccode)
-----------------------------------------------------------------------------------
                  |               Robust
       milcoupsum | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
robust_autocracy6 |   .5201246   .3125229     1.66   0.096    -.0924091    1.132658
           troops |  -1.954519   .9549712    -2.05   0.041    -3.826228   -.0828095
 aut_troops_round |   2.155091   .9260891     2.33   0.020     .3399892    3.970192
   population2000 |  -2.25e-09   6.35e-09    -0.35   0.723    -1.47e-08    1.02e-08
           gdpcap |  -.0196073   .0470524    -0.42   0.677    -.1118282    .0726137
          durable |  -.0225064     .02204    -1.02   0.307    -.0657039    .0206912
         conflict |   .9426648   .3748215     2.51   0.012     .2080282    1.677301
           milexp |  -384.2017   177.3931    -2.17   0.030    -731.8857    -36.5177
            exsol |   .9901354   2.155232     0.46   0.646    -3.234042    5.214312
            _cons |  -2.713176   .3788428    -7.16   0.000    -3.455694   -1.970658
-----------------------------------------------------------------------------------

. margins, dydx(aut_troops_round) post

Average marginal effects                                 Number of obs = 3,001
Model VCE: Robust

Expression: Predicted number of events, predict()
dy/dx wrt:  aut_troops_round

----------------------------------------------------------------------------------
                 |            Delta-method
                 |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
aut_troops_round |    .075403   .0348042     2.17   0.030      .007188    .1436181
----------------------------------------------------------------------------------

. est store m1

. 
. **model 2: anocracy
. poisson milcoupsum robust_anocracy5 c.troops ano_troops_round population2000 gdpcap durable conflict milexp exsol, cluster(ccode)

Iteration 0:   log pseudolikelihood = -418.11057  
Iteration 1:   log pseudolikelihood = -411.18452  
Iteration 2:   log pseudolikelihood = -406.65949  
Iteration 3:   log pseudolikelihood = -404.15667  
Iteration 4:   log pseudolikelihood = -404.07749  
Iteration 5:   log pseudolikelihood = -404.07276  
Iteration 6:   log pseudolikelihood = -404.07176  
Iteration 7:   log pseudolikelihood = -404.07153  
Iteration 8:   log pseudolikelihood = -404.07149  

Poisson regression                                     Number of obs =   3,001
                                                       Wald chi2(9)  = 1367.06
Log pseudolikelihood = -404.07149                      Prob > chi2   =  0.0000

                                    (Std. err. adjusted for 148 clusters in ccode)
----------------------------------------------------------------------------------
                 |               Robust
      milcoupsum | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
robust_anocracy5 |   .6332376   .2713354     2.33   0.020     .1014301    1.165045
          troops |   .0110435   .2334482     0.05   0.962    -.4465066    .4685937
ano_troops_round |  -13.74187   .4328845   -31.74   0.000    -14.59031   -12.89343
  population2000 |  -6.18e-10   5.86e-09    -0.11   0.916    -1.21e-08    1.09e-08
          gdpcap |  -.0122115   .0405742    -0.30   0.763    -.0917355    .0673124
         durable |  -.0151713   .0220104    -0.69   0.491     -.058311    .0279683
        conflict |   .9776341   .3511442     2.78   0.005      .289404    1.665864
          milexp |   -428.013   172.1062    -2.49   0.013    -765.3349   -90.69118
           exsol |   1.086092   2.426844     0.45   0.654    -3.670436    5.842619
           _cons |  -3.072781   .3497076    -8.79   0.000    -3.758195   -2.387366
----------------------------------------------------------------------------------

. margins, dydx(ano_troops_round) post

Average marginal effects                                 Number of obs = 3,001
Model VCE: Robust

Expression: Predicted number of events, predict()
dy/dx wrt:  ano_troops_round

----------------------------------------------------------------------------------
                 |            Delta-method
                 |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
ano_troops_round |  -.4808149   .0807926    -5.95   0.000    -.6391654   -.3224644
----------------------------------------------------------------------------------

. est store m2

. 
. **model 3: democracy
. eststo: poisson milcoupsum robust_democracy6 c.troops dem_troops_round population2000 gdpcap durable conflict milexp exsol, cluster(ccode)

Iteration 0:   log pseudolikelihood = -408.61739  
Iteration 1:   log pseudolikelihood = -403.74429  
Iteration 2:   log pseudolikelihood = -400.31999  
Iteration 3:   log pseudolikelihood = -397.87565  
Iteration 4:   log pseudolikelihood = -397.84952  
Iteration 5:   log pseudolikelihood = -397.84949  

Poisson regression                                      Number of obs =  3,001
                                                        Wald chi2(9)  = 109.54
Log pseudolikelihood = -397.84949                       Prob > chi2   = 0.0000

                                     (Std. err. adjusted for 148 clusters in ccode)
-----------------------------------------------------------------------------------
                  |               Robust
       milcoupsum | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
robust_democracy6 |  -1.262022   .3873756    -3.26   0.001    -2.021264   -.5027799
           troops |  -.1911396   .3855837    -0.50   0.620    -.9468699    .5645906
 dem_troops_round |  -.5818862   .4082854    -1.43   0.154    -1.382111    .2183385
   population2000 |  -5.75e-10   6.02e-09    -0.10   0.924    -1.24e-08    1.12e-08
           gdpcap |  -.0057685   .0293272    -0.20   0.844    -.0632488    .0517118
          durable |  -.0187527   .0218416    -0.86   0.391    -.0615614    .0240561
         conflict |   .8033519    .339413     2.37   0.018     .1381146    1.468589
           milexp |  -381.7162   162.2898    -2.35   0.019    -699.7983   -63.63404
            exsol |   .6623963   2.286806     0.29   0.772    -3.819662    5.144454
            _cons |  -2.370042   .3220727    -7.36   0.000    -3.001293   -1.738791
-----------------------------------------------------------------------------------
(est9 stored)

. margins, dydx(dem_troops_round) post

Average marginal effects                                 Number of obs = 3,001
Model VCE: Robust

Expression: Predicted number of events, predict()
dy/dx wrt:  dem_troops_round

----------------------------------------------------------------------------------
                 |            Delta-method
                 |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
dem_troops_round |  -.0203592   .0149448    -1.36   0.173    -.0496505     .008932
----------------------------------------------------------------------------------

. est store m3

. 
. *coefplot
. coefplot (m1, label(Autocracy * Peacekeepers) lcolor(black) lwidth(thick) lpattern(dash))  (m2, label(Anocracy * Peacekeepers)) (m3, label(Democracy * Peacekeepers)), xline(0) xtitle(Average marginal effects)

. 
. 
end of do-file

. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. graph save "figures\Study_2\Figure_2_original.gph", replace
(file figures\Study_2\Figure_2_original.gph not found)
file figures\Study_2\Figure_2_original.gph saved

. 
end of do-file

. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. *model 2a: autocracy
. poisson milcoupsum robust_autocracy6##c.troops population2000 gdpcap durable conflict milexp exsol, cluster(ccode)

Iteration 0:   log pseudolikelihood = -449.22112  
Iteration 1:   log pseudolikelihood = -410.06911  
Iteration 2:   log pseudolikelihood = -404.01836  
Iteration 3:   log pseudolikelihood = -402.11343  
Iteration 4:   log pseudolikelihood = -402.01812  
Iteration 5:   log pseudolikelihood = -402.01764  
Iteration 6:   log pseudolikelihood = -402.01764  

Poisson regression                                      Number of obs =  3,001
                                                        Wald chi2(9)  =  82.80
Log pseudolikelihood = -402.01764                       Prob > chi2   = 0.0000

                                              (Std. err. adjusted for 148 clusters in ccode)
--------------------------------------------------------------------------------------------
                           |               Robust
                milcoupsum | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------------+----------------------------------------------------------------
       1.robust_autocracy6 |   .5278635   .3128468     1.69   0.092    -.0853049    1.141032
                    troops |  -1.838259   1.027145    -1.79   0.074    -3.851427    .1749095
                           |
robust_autocracy6#c.troops |
                        1  |   2.053367   1.009086     2.03   0.042     .0755952    4.031138
                           |
            population2000 |  -2.34e-09   6.43e-09    -0.36   0.716    -1.49e-08    1.03e-08
                    gdpcap |   -.020064   .0477717    -0.42   0.674    -.1136947    .0735667
                   durable |  -.0224258   .0221588    -1.01   0.312    -.0658563    .0210046
                  conflict |   .9358275   .3751772     2.49   0.013     .2004936    1.671161
                    milexp |  -378.6024   181.1369    -2.09   0.037    -733.6242   -23.58055
                     exsol |   .9254581   2.147148     0.43   0.666    -3.282875    5.133792
                     _cons |  -2.714514   .3781303    -7.18   0.000    -3.455636   -1.973392
--------------------------------------------------------------------------------------------

. margins, dydx(robust_autocracy6) post

Average marginal effects                                 Number of obs = 3,001
Model VCE: Robust

Expression: Predicted number of events, predict()
dy/dx wrt:  1.robust_autocracy6

-------------------------------------------------------------------------------------
                    |            Delta-method
                    |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
--------------------+----------------------------------------------------------------
1.robust_autocracy6 |   .0325869   .0163034     2.00   0.046     .0006328     .064541
-------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. est store m2a

. 
end of do-file

. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. 
. *model 4a: anocracy
. poisson milcoupsum robust_anocracy5##c.troops population2000 gdpcap durable conflict milexp exsol, cluster(ccode)

Iteration 0:   log pseudolikelihood = -418.40873  
Iteration 1:   log pseudolikelihood =  -412.0033  
Iteration 2:   log pseudolikelihood =   -407.598  
Iteration 3:   log pseudolikelihood = -405.16414  
Iteration 4:   log pseudolikelihood = -405.09412  
Iteration 5:   log pseudolikelihood = -405.09351  
Iteration 6:   log pseudolikelihood = -405.09351  

Poisson regression                                      Number of obs =  3,001
                                                        Wald chi2(9)  =  47.18
Log pseudolikelihood = -405.09351                       Prob > chi2   = 0.0000

                                             (Std. err. adjusted for 148 clusters in ccode)
-------------------------------------------------------------------------------------------
                          |               Robust
               milcoupsum | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------------------+----------------------------------------------------------------
       1.robust_anocracy5 |   .6782844   .2786318     2.43   0.015     .1321761    1.224393
                   troops |   .0150535   .2278477     0.07   0.947    -.4315197    .4616267
                          |
robust_anocracy5#c.troops |
                       1  |  -2.774543   .7840732    -3.54   0.000    -4.311299   -1.237788
                          |
           population2000 |  -4.52e-10   5.94e-09    -0.08   0.939    -1.21e-08    1.12e-08
                   gdpcap |  -.0128145   .0410951    -0.31   0.755    -.0933595    .0677304
                  durable |  -.0147644     .02168    -0.68   0.496    -.0572563    .0277276
                 conflict |   .9400657   .3531598     2.66   0.008     .2478853    1.632246
                   milexp |  -426.8629   172.5747    -2.47   0.013    -765.1032   -88.62272
                    exsol |   1.080705   2.444078     0.44   0.658    -3.709601     5.87101
                    _cons |  -3.068939   .3479924    -8.82   0.000    -3.750991   -2.386886
-------------------------------------------------------------------------------------------

. margins, dydx(robust_anocracy5) post

Average marginal effects                                 Number of obs = 3,001
Model VCE: Robust

Expression: Predicted number of events, predict()
dy/dx wrt:  1.robust_anocracy5

------------------------------------------------------------------------------------
                   |            Delta-method
                   |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------------+----------------------------------------------------------------
1.robust_anocracy5 |   .0183265   .0096998     1.89   0.059    -.0006847    .0373377
------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. est store m4a

. 
end of do-file

. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. 
. *model 8a: New democracy
. eststo: poisson milcoupsum new_robust_democracy6##c.troops population2000 gdpcap durable conflict milexp exsol, cluster(ccode)

Iteration 0:   log pseudolikelihood = -411.78171  
Iteration 1:   log pseudolikelihood = -406.87226  
Iteration 2:   log pseudolikelihood = -403.34415  
Iteration 3:   log pseudolikelihood = -400.72867  
Iteration 4:   log pseudolikelihood = -400.69906  
Iteration 5:   log pseudolikelihood = -400.69903  

Poisson regression                                      Number of obs =  3,001
                                                        Wald chi2(9)  =  72.73
Log pseudolikelihood = -400.69903                       Prob > chi2   = 0.0000

                                                  (Std. err. adjusted for 148 clusters in ccode)
------------------------------------------------------------------------------------------------
                               |               Robust
                    milcoupsum | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------------------------+----------------------------------------------------------------
       1.new_robust_democracy6 |  -1.118677   .4094854    -2.73   0.006    -1.921254   -.3161008
                        troops |  -.1720984   .3543506    -0.49   0.627    -.8666128    .5224159
                               |
new_robust_democracy6#c.troops |
                            1  |  -.6602458   .7434278    -0.89   0.374    -2.117338     .796846
                               |
                population2000 |  -3.33e-10   6.06e-09    -0.05   0.956    -1.22e-08    1.15e-08
                        gdpcap |   -.007618   .0313899    -0.24   0.808    -.0691412    .0539051
                       durable |  -.0167517    .021227    -0.79   0.430    -.0583558    .0248523
                      conflict |   .8082747   .3447763     2.34   0.019     .1325255    1.484024
                        milexp |  -398.4421   171.2426    -2.33   0.020    -734.0715   -62.81271
                         exsol |   .7746237   2.123611     0.36   0.715    -3.387578    4.936825
                         _cons |  -2.430278    .319332    -7.61   0.000    -3.056157   -1.804399
------------------------------------------------------------------------------------------------
(est10 stored)

. margins, dydx(new_robust_democracy6) post

Average marginal effects                                 Number of obs = 3,001
Model VCE: Robust

Expression: Predicted number of events, predict()
dy/dx wrt:  1.new_robust_democracy6

-----------------------------------------------------------------------------------------
                        |            Delta-method
                        |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
------------------------+----------------------------------------------------------------
1.new_robust_democracy6 |  -.0333935    .009051    -3.69   0.000    -.0511331   -.0156538
-----------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. est store m8a

. 
end of do-file

. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. 
. *coefplot
. coefplot (m2a, label(Autocracy) offset(-0.1)) ///
> (m4a, label(Anocracy) offset(-0.1)) ///
> (m8a, label(New Democracy) offset(-0.1)), xline(0) ///
> title("", size(medsmall) span) xtitle("Average Marginal Effects", size(small)) ylabel(,labsize(vsmall)) xlabel(,labsize(vsmall)) legend(size(vsmall) span)

. 
end of do-file

. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. 
. *coefplot
. coefplot (m2a, label(Autocracy) offset(-0.1)) ///
> (m4a, label(Anocracy) offset(-0.1)) ///
> (m8a, label(New Democracy) offset(-0.1)), xline(0) ///
> title("", size(medsmall) span) xtitle("Average Marginal Effects", size(small)) ylabel(,labsize(vsmall)) xlabel(,labsize(vsmall)) legend(size(vsmall) span)

. 
. graph export "figures\Study_2\Figure_2_AME_Regime_Type.png", replace
(file figures\Study_2\Figure_2_AME_Regime_Type.png not found)
file figures\Study_2\Figure_2_AME_Regime_Type.png saved as PNG format

. 
end of do-file

. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. 
.         
. *model 2a: autocracy
. poisson milcoupsum robust_autocracy6##c.troops population2000 gdpcap durable conflict milexp exsol, cluster(ccode)

Iteration 0:   log pseudolikelihood = -449.22112  
Iteration 1:   log pseudolikelihood = -410.06911  
Iteration 2:   log pseudolikelihood = -404.01836  
Iteration 3:   log pseudolikelihood = -402.11343  
Iteration 4:   log pseudolikelihood = -402.01812  
Iteration 5:   log pseudolikelihood = -402.01764  
Iteration 6:   log pseudolikelihood = -402.01764  

Poisson regression                                      Number of obs =  3,001
                                                        Wald chi2(9)  =  82.80
Log pseudolikelihood = -402.01764                       Prob > chi2   = 0.0000

                                              (Std. err. adjusted for 148 clusters in ccode)
--------------------------------------------------------------------------------------------
                           |               Robust
                milcoupsum | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------------+----------------------------------------------------------------
       1.robust_autocracy6 |   .5278635   .3128468     1.69   0.092    -.0853049    1.141032
                    troops |  -1.838259   1.027145    -1.79   0.074    -3.851427    .1749095
                           |
robust_autocracy6#c.troops |
                        1  |   2.053367   1.009086     2.03   0.042     .0755952    4.031138
                           |
            population2000 |  -2.34e-09   6.43e-09    -0.36   0.716    -1.49e-08    1.03e-08
                    gdpcap |   -.020064   .0477717    -0.42   0.674    -.1136947    .0735667
                   durable |  -.0224258   .0221588    -1.01   0.312    -.0658563    .0210046
                  conflict |   .9358275   .3751772     2.49   0.013     .2004936    1.671161
                    milexp |  -378.6024   181.1369    -2.09   0.037    -733.6242   -23.58055
                     exsol |   .9254581   2.147148     0.43   0.666    -3.282875    5.133792
                     _cons |  -2.714514   .3781303    -7.18   0.000    -3.455636   -1.973392
--------------------------------------------------------------------------------------------

. margins, dydx(robust_autocracy6) at (troops = (0(0.05)9.8)) post

Average marginal effects                                 Number of obs = 3,001
Model VCE: Robust

Expression: Predicted number of events, predict()
dy/dx wrt:  1.robust_autocracy6
1._at:   troops =    0
2._at:   troops =  .05
3._at:   troops =   .1
4._at:   troops =  .15
5._at:   troops =   .2
6._at:   troops =  .25
7._at:   troops =   .3
8._at:   troops =  .35
9._at:   troops =   .4
10._at:  troops =  .45
11._at:  troops =   .5
12._at:  troops =  .55
13._at:  troops =   .6
14._at:  troops =  .65
15._at:  troops =   .7
16._at:  troops =  .75
17._at:  troops =   .8
18._at:  troops =  .85
19._at:  troops =   .9
20._at:  troops =  .95
21._at:  troops =    1
22._at:  troops = 1.05
23._at:  troops =  1.1
24._at:  troops = 1.15
25._at:  troops =  1.2
26._at:  troops = 1.25
27._at:  troops =  1.3
28._at:  troops = 1.35
29._at:  troops =  1.4
30._at:  troops = 1.45
31._at:  troops =  1.5
32._at:  troops = 1.55
33._at:  troops =  1.6
34._at:  troops = 1.65
35._at:  troops =  1.7
36._at:  troops = 1.75
37._at:  troops =  1.8
38._at:  troops = 1.85
39._at:  troops =  1.9
40._at:  troops = 1.95
41._at:  troops =    2
42._at:  troops = 2.05
43._at:  troops =  2.1
44._at:  troops = 2.15
45._at:  troops =  2.2
46._at:  troops = 2.25
47._at:  troops =  2.3
48._at:  troops = 2.35
49._at:  troops =  2.4
50._at:  troops = 2.45
51._at:  troops =  2.5
52._at:  troops = 2.55
53._at:  troops =  2.6
54._at:  troops = 2.65
55._at:  troops =  2.7
56._at:  troops = 2.75
57._at:  troops =  2.8
58._at:  troops = 2.85
59._at:  troops =  2.9
60._at:  troops = 2.95
61._at:  troops =    3
62._at:  troops = 3.05
63._at:  troops =  3.1
64._at:  troops = 3.15
65._at:  troops =  3.2
66._at:  troops = 3.25
67._at:  troops =  3.3
68._at:  troops = 3.35
69._at:  troops =  3.4
70._at:  troops = 3.45
71._at:  troops =  3.5
72._at:  troops = 3.55
73._at:  troops =  3.6
74._at:  troops = 3.65
75._at:  troops =  3.7
76._at:  troops = 3.75
77._at:  troops =  3.8
78._at:  troops = 3.85
79._at:  troops =  3.9
80._at:  troops = 3.95
81._at:  troops =    4
82._at:  troops = 4.05
83._at:  troops =  4.1
84._at:  troops = 4.15
85._at:  troops =  4.2
86._at:  troops = 4.25
87._at:  troops =  4.3
88._at:  troops = 4.35
89._at:  troops =  4.4
90._at:  troops = 4.45
91._at:  troops =  4.5
92._at:  troops = 4.55
93._at:  troops =  4.6
94._at:  troops = 4.65
95._at:  troops =  4.7
96._at:  troops = 4.75
97._at:  troops =  4.8
98._at:  troops = 4.85
99._at:  troops =  4.9
100._at: troops = 4.95
101._at: troops =    5
102._at: troops = 5.05
103._at: troops =  5.1
104._at: troops = 5.15
105._at: troops =  5.2
106._at: troops = 5.25
107._at: troops =  5.3
108._at: troops = 5.35
109._at: troops =  5.4
110._at: troops = 5.45
111._at: troops =  5.5
112._at: troops = 5.55
113._at: troops =  5.6
114._at: troops = 5.65
115._at: troops =  5.7
116._at: troops = 5.75
117._at: troops =  5.8
118._at: troops = 5.85
119._at: troops =  5.9
120._at: troops = 5.95
121._at: troops =    6
122._at: troops = 6.05
123._at: troops =  6.1
124._at: troops = 6.15
125._at: troops =  6.2
126._at: troops = 6.25
127._at: troops =  6.3
128._at: troops = 6.35
129._at: troops =  6.4
130._at: troops = 6.45
131._at: troops =  6.5
132._at: troops = 6.55
133._at: troops =  6.6
134._at: troops = 6.65
135._at: troops =  6.7
136._at: troops = 6.75
137._at: troops =  6.8
138._at: troops = 6.85
139._at: troops =  6.9
140._at: troops = 6.95
141._at: troops =    7
142._at: troops = 7.05
143._at: troops =  7.1
144._at: troops = 7.15
145._at: troops =  7.2
146._at: troops = 7.25
147._at: troops =  7.3
148._at: troops = 7.35
149._at: troops =  7.4
150._at: troops = 7.45
151._at: troops =  7.5
152._at: troops = 7.55
153._at: troops =  7.6
154._at: troops = 7.65
155._at: troops =  7.7
156._at: troops = 7.75
157._at: troops =  7.8
158._at: troops = 7.85
159._at: troops =  7.9
160._at: troops = 7.95
161._at: troops =    8
162._at: troops = 8.05
163._at: troops =  8.1
164._at: troops = 8.15
165._at: troops =  8.2
166._at: troops = 8.25
167._at: troops =  8.3
168._at: troops = 8.35
169._at: troops =  8.4
170._at: troops = 8.45
171._at: troops =  8.5
172._at: troops = 8.55
173._at: troops =  8.6
174._at: troops = 8.65
175._at: troops =  8.7
176._at: troops = 8.75
177._at: troops =  8.8
178._at: troops = 8.85
179._at: troops =  8.9
180._at: troops = 8.95
181._at: troops =    9
182._at: troops = 9.05
183._at: troops =  9.1
184._at: troops = 9.15
185._at: troops =  9.2
186._at: troops = 9.25
187._at: troops =  9.3
188._at: troops = 9.35
189._at: troops =  9.4
190._at: troops = 9.45
191._at: troops =  9.5
192._at: troops = 9.55
193._at: troops =  9.6
194._at: troops = 9.65
195._at: troops =  9.7
196._at: troops = 9.75
197._at: troops =  9.8

--------------------------------------------------------------------------------------
                     |            Delta-method
                     |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
---------------------+----------------------------------------------------------------
0.robust_autocracy6  |  (base outcome)
---------------------+----------------------------------------------------------------
1.robust_autocracy6  |
                 _at |
                  1  |   .0240775   .0166719     1.44   0.149    -.0085989    .0567539
                  2  |   .0277532   .0164575     1.69   0.092    -.0045028    .0600093
                  3  |   .0311688   .0164246     1.90   0.058    -.0010229    .0633605
                  4  |   .0343477   .0164992     2.08   0.037       .00201    .0666855
                  5  |   .0373115    .016629     2.24   0.025     .0047192    .0699037
                  6  |   .0400796   .0167799     2.39   0.017     .0071917    .0729675
                  7  |     .04267   .0169309     2.52   0.012      .009486     .075854
                  8  |    .045099   .0170705     2.64   0.008     .0116414    .0785566
                  9  |   .0473815   .0171934     2.76   0.006     .0136831    .0810799
                 10  |    .049531   .0172982     2.86   0.004     .0156271     .083435
                 11  |   .0515601   .0173863     2.97   0.003     .0174835    .0856366
                 12  |   .0534799   .0174603     3.06   0.002     .0192584    .0877014
                 13  |   .0553008   .0175235     3.16   0.002     .0209553    .0896464
                 14  |   .0570324   .0175799     3.24   0.001     .0225765    .0914883
                 15  |   .0586831   .0176329     3.33   0.001     .0241232     .093243
                 16  |   .0602608   .0176862     3.41   0.001     .0255965    .0949252
                 17  |   .0617728   .0177429     3.48   0.000     .0269973    .0965483
                 18  |   .0632256   .0178059     3.55   0.000     .0283266    .0981245
                 19  |   .0646252   .0178776     3.61   0.000     .0295857    .0996646
                 20  |    .065977     .01796     3.67   0.000      .030776     .101178
                 21  |   .0672861   .0180549     3.73   0.000     .0318992    .1026731
                 22  |   .0685571   .0181636     3.77   0.000     .0329571    .1041571
                 23  |   .0697941   .0182872     3.82   0.000     .0339518    .1056364
                 24  |   .0710009   .0184265     3.85   0.000     .0348856    .1071162
                 25  |   .0721811   .0185821     3.88   0.000     .0357608    .1086014
                 26  |   .0733378   .0187544     3.91   0.000     .0365798    .1100958
                 27  |    .074474   .0189436     3.93   0.000     .0373452    .1116029
                 28  |   .0755924   .0191499     3.95   0.000     .0380592    .1131256
                 29  |   .0766953   .0193733     3.96   0.000     .0387244    .1146663
                 30  |   .0777852   .0196137     3.97   0.000      .039343    .1162273
                 31  |   .0788639    .019871     3.97   0.000     .0399173    .1178104
                 32  |   .0799334   .0201452     3.97   0.000     .0404495    .1194173
                 33  |   .0809955   .0204361     3.96   0.000     .0409415    .1210494
                 34  |   .0820516   .0207434     3.96   0.000     .0413953     .122708
                 35  |   .0831034   .0210671     3.94   0.000     .0418126    .1243942
                 36  |   .0841521   .0214071     3.93   0.000      .042195    .1261091
                 37  |   .0851989   .0217631     3.91   0.000     .0425441    .1278538
                 38  |   .0862451   .0221351     3.90   0.000     .0428611     .129629
                 39  |   .0872916    .022523     3.88   0.000     .0431474    .1314358
                 40  |   .0883394   .0229266     3.85   0.000      .043404    .1332748
                 41  |   .0893894   .0233461     3.83   0.000      .043632    .1351469
                 42  |   .0904425   .0237812     3.80   0.000     .0438322    .1370528
                 43  |   .0914993   .0242321     3.78   0.000     .0440053    .1389933
                 44  |   .0925607   .0246986     3.75   0.000     .0441522    .1409691
                 45  |   .0936272   .0251809     3.72   0.000     .0442734    .1429809
                 46  |   .0946995    .025679     3.69   0.000     .0443695    .1450295
                 47  |   .0957781    .026193     3.66   0.000     .0444408    .1471154
                 48  |   .0968636   .0267228     3.62   0.000     .0444879    .1492393
                 49  |   .0979565   .0272687     3.59   0.000     .0445109     .151402
                 50  |   .0990571   .0278306     3.56   0.000     .0445102    .1536041
                 51  |   .1001661   .0284088     3.53   0.000      .044486    .1558462
                 52  |   .1012837   .0290032     3.49   0.000     .0444384     .158129
                 53  |   .1024104   .0296142     3.46   0.001     .0443677    .1604531
                 54  |   .1035465   .0302417     3.42   0.001     .0442738    .1628192
                 55  |   .1046924    .030886     3.39   0.001     .0441569    .1652278
                 56  |   .1058483   .0315472     3.36   0.001     .0440169    .1676797
                 57  |   .1070147   .0322255     3.32   0.001     .0438539    .1701754
                 58  |   .1081917   .0329209     3.29   0.001     .0436679    .1727156
                 59  |   .1093798   .0336338     3.25   0.001     .0434587    .1753008
                 60  |   .1105791   .0343643     3.22   0.001     .0432264    .1779318
                 61  |   .1117899   .0351125     3.18   0.001     .0429707    .1806091
                 62  |   .1130125   .0358786     3.15   0.002     .0426917    .1833333
                 63  |   .1142471   .0366629     3.12   0.002     .0423891     .186105
                 64  |   .1154939   .0374655     3.08   0.002     .0420629     .188925
                 65  |   .1167533   .0382867     3.05   0.002     .0417128    .1917938
                 66  |   .1180253   .0391266     3.02   0.003     .0413386    .1947119
                 67  |   .1193102   .0399854     2.98   0.003     .0409403    .1976802
                 68  |   .1206083   .0408634     2.95   0.003     .0405175    .2006991
                 69  |   .1219197   .0417608     2.92   0.004     .0400701    .2037693
                 70  |   .1232447   .0426777     2.89   0.004     .0395979    .2068915
                 71  |   .1245834   .0436145     2.86   0.004     .0391005    .2100663
                 72  |   .1259361   .0445714     2.83   0.005     .0385778    .2132944
                 73  |   .1273029   .0455485     2.79   0.005     .0380294    .2165763
                 74  |    .128684   .0465462     2.76   0.006     .0374551    .2199129
                 75  |   .1300797   .0475647     2.73   0.006     .0368547    .2233047
                 76  |   .1314901   .0486041     2.71   0.007     .0362278    .2267524
                 77  |   .1329154   .0496649     2.68   0.007     .0355741    .2302568
                 78  |   .1343559   .0507471     2.65   0.008     .0348933    .2338184
                 79  |   .1358116   .0518512     2.62   0.009     .0341851    .2374381
                 80  |   .1372828   .0529773     2.59   0.010     .0334492    .2411165
                 81  |   .1387697   .0541258     2.56   0.010     .0326852    .2448543
                 82  |   .1402725   .0552968     2.54   0.011     .0318927    .2486523
                 83  |   .1417913   .0564908     2.51   0.012     .0310714    .2525112
                 84  |   .1433264   .0577079     2.48   0.013      .030221    .2564318
                 85  |   .1448779   .0589485     2.46   0.014      .029341    .2604147
                 86  |    .146446   .0602128     2.43   0.015     .0284311    .2644609
                 87  |    .148031   .0615012     2.41   0.016     .0274909    .2685711
                 88  |    .149633   .0628139     2.38   0.017     .0265199     .272746
                 89  |   .1512522   .0641514     2.36   0.018     .0255178    .2769866
                 90  |   .1528888   .0655138     2.33   0.020     .0244841    .2812935
                 91  |    .154543   .0669015     2.31   0.021     .0234184    .2856676
                 92  |    .156215   .0683149     2.29   0.022     .0223203    .2901098
                 93  |    .157905   .0697543     2.26   0.024     .0211892    .2946209
                 94  |   .1596133   .0712199     2.24   0.025     .0200248    .2992018
                 95  |   .1613399   .0727123     2.22   0.026     .0188265    .3038533
                 96  |   .1630851   .0742316     2.20   0.028     .0175939    .3085764
                 97  |   .1648492   .0757783     2.18   0.030     .0163265    .3133719
                 98  |   .1666323   .0773527     2.15   0.031     .0150238    .3182408
                 99  |   .1684346   .0789552     2.13   0.033     .0136852     .323184
                100  |   .1702564   .0805862     2.11   0.035     .0123104    .3282024
                101  |   .1720978    .082246     2.09   0.036     .0108986    .3332971
                102  |   .1739592   .0839351     2.07   0.038     .0094494    .3384689
                103  |   .1758406   .0856537     2.05   0.040     .0079623    .3437188
                104  |   .1777423   .0874024     2.03   0.042     .0064367    .3490479
                105  |   .1796646   .0891815     2.01   0.044      .004872    .3544572
                106  |   .1816076   .0909915     2.00   0.046     .0032676    .3599477
                107  |   .1835716   .0928327     1.98   0.048     .0016229    .3655204
                108  |   .1855569   .0947055     1.96   0.050    -.0000626    .3711763
                109  |   .1875636   .0966105     1.94   0.052    -.0017896    .3769167
                110  |   .1895919    .098548     1.92   0.054    -.0035586    .3827425
                111  |   .1916422   .1005185     1.91   0.057    -.0053704    .3886549
                112  |   .1937147   .1025224     1.89   0.059    -.0072256     .394655
                113  |   .1958095   .1045602     1.87   0.061    -.0091247    .4007438
                114  |    .197927   .1066324     1.86   0.063    -.0110686    .4069226
                115  |   .2000674   .1087393     1.84   0.066    -.0130578    .4131926
                116  |   .2022309   .1108816     1.82   0.068     -.015093    .4195549
                117  |   .2044178   .1130596     1.81   0.071     -.017175    .4260107
                118  |   .2066284   .1152739     1.79   0.073    -.0193044    .4325611
                119  |   .2088628    .117525     1.78   0.076     -.021482    .4392076
                120  |   .2111214   .1198134     1.76   0.078    -.0237085    .4459513
                121  |   .2134044   .1221395     1.75   0.081    -.0259847    .4527935
                122  |   .2157121    .124504     1.73   0.083    -.0283112    .4597354
                123  |   .2180447   .1269073     1.72   0.086    -.0306889    .4667784
                124  |   .2204026   .1293499     1.70   0.088    -.0331186    .4739238
                125  |   .2227859   .1318325     1.69   0.091     -.035601    .4811729
                126  |   .2251951   .1343555     1.68   0.094    -.0381369    .4885271
                127  |   .2276303   .1369196     1.66   0.096    -.0407273    .4959878
                128  |   .2300917   .1395253     1.65   0.099    -.0433728    .5035562
                129  |   .2325799   .1421731     1.64   0.102    -.0460743     .511234
                130  |   .2350949   .1448637     1.62   0.105    -.0488327    .5190224
                131  |   .2376371   .1475976     1.61   0.107    -.0516489    .5269231
                132  |   .2402068   .1503755     1.60   0.110    -.0545237    .5349373
                133  |   .2428043   .1531979     1.58   0.113    -.0574581    .5430666
                134  |   .2454298   .1560655     1.57   0.116    -.0604529    .5513125
                135  |   .2480838   .1589788     1.56   0.119     -.063509    .5596766
                136  |   .2507665   .1619386     1.55   0.121    -.0666275    .5681604
                137  |   .2534781   .1649455     1.54   0.124    -.0698092    .5767654
                138  |   .2562191   .1680001     1.53   0.127    -.0730551    .5854933
                139  |   .2589897   .1711031     1.51   0.130    -.0763662    .5943457
                140  |   .2617903   .1742551     1.50   0.133    -.0797435    .6033241
                141  |   .2646212   .1774569     1.49   0.136     -.083188    .6124303
                142  |   .2674827   .1807091     1.48   0.139    -.0867007     .621666
                143  |   .2703751   .1840124     1.47   0.142    -.0902827    .6310328
                144  |   .2732988   .1873676     1.46   0.145     -.093935    .6405325
                145  |   .2762541   .1907753     1.45   0.148    -.0976586    .6501667
                146  |   .2792413   .1942362     1.44   0.151    -.1014547    .6599374
                147  |   .2822609   .1977512     1.43   0.153    -.1053244    .6698462
                148  |   .2853131    .201321     1.42   0.156    -.1092688     .679895
                149  |   .2883983   .2049463     1.41   0.159    -.1132889    .6900856
                150  |   .2915169   .2086278     1.40   0.162     -.117386    .7004198
                151  |   .2946692   .2123664     1.39   0.165    -.1215613    .7108997
                152  |   .2978556   .2161629     1.38   0.168    -.1258159    .7215271
                153  |   .3010765    .220018     1.37   0.171    -.1301509    .7323038
                154  |   .3043321   .2239326     1.36   0.174    -.1345677     .743232
                155  |    .307623   .2279075     1.35   0.177    -.1390674    .7543135
                156  |   .3109495   .2319435     1.34   0.180    -.1436514    .7655504
                157  |   .3143119   .2360415     1.33   0.183    -.1483209    .7769447
                158  |   .3177107   .2402022     1.32   0.186     -.153077    .7884985
                159  |   .3211463   .2444267     1.31   0.189    -.1579213    .8002139
                160  |    .324619   .2487158     1.31   0.192     -.162855    .8120929
                161  |   .3281292   .2530703     1.30   0.195    -.1678794    .8241378
                162  |   .3316774   .2574912     1.29   0.198    -.1729959    .8363508
                163  |    .335264   .2619793     1.28   0.201     -.178206     .848734
                164  |   .3388893   .2665356     1.27   0.204    -.1835108    .8612895
                165  |   .3425539   .2711611     1.26   0.206     -.188912    .8740198
                166  |   .3462581   .2758567     1.26   0.209     -.194411    .8869272
                167  |   .3500024   .2806233     1.25   0.212    -.2000092     .900014
                168  |   .3537871    .285462     1.24   0.215    -.2057081    .9132824
                169  |   .3576127   .2903736     1.23   0.218    -.2115091    .9267345
                170  |   .3614798   .2953594     1.22   0.221    -.2174139    .9403735
                171  |   .3653886   .3004201     1.22   0.224     -.223424    .9542013
                172  |   .3693397    .305557     1.21   0.227     -.229541    .9682205
                173  |   .3733336    .310771     1.20   0.230    -.2357664    .9824336
                174  |   .3773706   .3160631     1.19   0.232    -.2421017    .9968428
                175  |   .3814513   .3214345     1.19   0.235    -.2485488    1.011451
                176  |   .3855761   .3268863     1.18   0.238    -.2551092    1.026261
                177  |   .3897455   .3324195     1.17   0.241    -.2617847    1.041276
                178  |     .39396   .3380352     1.17   0.244    -.2685769    1.056497
                179  |     .39822   .3437346     1.16   0.247    -.2754874    1.071927
                180  |   .4025261   .3495189     1.15   0.249    -.2825182    1.087571
                181  |   .4068789   .3553891     1.14   0.252     -.289671    1.103429
                182  |   .4112786   .3613466     1.14   0.255    -.2969476    1.119505
                183  |    .415726   .3673924     1.13   0.258    -.3043498    1.135802
                184  |   .4202214   .3735276     1.13   0.261    -.3118794    1.152322
                185  |   .4247654   .3797538     1.12   0.263    -.3195384    1.169069
                186  |   .4293586    .386072     1.11   0.266    -.3273286    1.186046
                187  |   .4340015   .3924835     1.11   0.269     -.335252    1.203255
                188  |   .4386945   .3989895     1.10   0.272    -.3433105      1.2207
                189  |   .4434383   .4055913     1.09   0.274     -.351506    1.238383
                190  |   .4482334   .4122903     1.09   0.277    -.3598408    1.256308
                191  |   .4530803   .4190878     1.08   0.280    -.3683167    1.274477
                192  |   .4579797   .4259851     1.08   0.282    -.3769358    1.292895
                193  |   .4629321   .4329836     1.07   0.285    -.3857002    1.311564
                194  |   .4679379   .4400845     1.06   0.288    -.3946119    1.330488
                195  |   .4729979   .4472895     1.06   0.290    -.4036734    1.349669
                196  |   .4781127   .4545999     1.05   0.293    -.4128867    1.369112
                197  |   .4832828    .462017     1.05   0.296    -.4222539    1.388819
--------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. marginsplot, ///
>         recast(line) plot1opts(lcolor(gs8)) ciopt(color(black%20)) recastci(rarea) ///
>         ytitle("AME", size(medsmall)) ///
>         xtitle("Troops", size(medsmall))  ///
>         title("Autocracy", size(medium)) yline(0, lcolor(red)) ///
>         addplot(hist troops, /// adding histogram marginsplot graph
> blcolor(black%30) fcolor(white%10) /// bar line and fill colors
> percent /// histogram bins in "percent" rather than "discrete" (actual)
> yaxis(2) /// calls for 2nd y-axis
> yscale(alt lcolor() axis(2)) /// scaling on 2nd y-axis
> ylabel(0 "0%" 20 "20%" 40 "40%" 60 "60%" 80 "80%" 100 "100%", /// labels on 2nd y-axis
> labcolor() axis(2) tlcolor(black) tlwidth(thin) labsize(small)) /// label options on 2nd y-axis
> ytitle(" ", axis(2)))  legend(off)

Variables that uniquely identify margins: troops

. graph save "figures\Study_2\B2.2.gph", replace
file figures\Study_2\B2.2.gph saved

. 
. 
. *model 4a: anocracy
. poisson milcoupsum robust_anocracy5##c.troops population2000 gdpcap durable conflict milexp exsol, cluster(ccode)

Iteration 0:   log pseudolikelihood = -418.40873  
Iteration 1:   log pseudolikelihood =  -412.0033  
Iteration 2:   log pseudolikelihood =   -407.598  
Iteration 3:   log pseudolikelihood = -405.16414  
Iteration 4:   log pseudolikelihood = -405.09412  
Iteration 5:   log pseudolikelihood = -405.09351  
Iteration 6:   log pseudolikelihood = -405.09351  

Poisson regression                                      Number of obs =  3,001
                                                        Wald chi2(9)  =  47.18
Log pseudolikelihood = -405.09351                       Prob > chi2   = 0.0000

                                             (Std. err. adjusted for 148 clusters in ccode)
-------------------------------------------------------------------------------------------
                          |               Robust
               milcoupsum | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------------------+----------------------------------------------------------------
       1.robust_anocracy5 |   .6782844   .2786318     2.43   0.015     .1321761    1.224393
                   troops |   .0150535   .2278477     0.07   0.947    -.4315197    .4616267
                          |
robust_anocracy5#c.troops |
                       1  |  -2.774543   .7840732    -3.54   0.000    -4.311299   -1.237788
                          |
           population2000 |  -4.52e-10   5.94e-09    -0.08   0.939    -1.21e-08    1.12e-08
                   gdpcap |  -.0128145   .0410951    -0.31   0.755    -.0933595    .0677304
                  durable |  -.0147644     .02168    -0.68   0.496    -.0572563    .0277276
                 conflict |   .9400657   .3531598     2.66   0.008     .2478853    1.632246
                   milexp |  -426.8629   172.5747    -2.47   0.013    -765.1032   -88.62272
                    exsol |   1.080705   2.444078     0.44   0.658    -3.709601     5.87101
                    _cons |  -3.068939   .3479924    -8.82   0.000    -3.750991   -2.386886
-------------------------------------------------------------------------------------------

. margins, dydx(robust_anocracy5) at (troops =  (0(0.05)9.8)) post

Average marginal effects                                 Number of obs = 3,001
Model VCE: Robust

Expression: Predicted number of events, predict()
dy/dx wrt:  1.robust_anocracy5
1._at:   troops =    0
2._at:   troops =  .05
3._at:   troops =   .1
4._at:   troops =  .15
5._at:   troops =   .2
6._at:   troops =  .25
7._at:   troops =   .3
8._at:   troops =  .35
9._at:   troops =   .4
10._at:  troops =  .45
11._at:  troops =   .5
12._at:  troops =  .55
13._at:  troops =   .6
14._at:  troops =  .65
15._at:  troops =   .7
16._at:  troops =  .75
17._at:  troops =   .8
18._at:  troops =  .85
19._at:  troops =   .9
20._at:  troops =  .95
21._at:  troops =    1
22._at:  troops = 1.05
23._at:  troops =  1.1
24._at:  troops = 1.15
25._at:  troops =  1.2
26._at:  troops = 1.25
27._at:  troops =  1.3
28._at:  troops = 1.35
29._at:  troops =  1.4
30._at:  troops = 1.45
31._at:  troops =  1.5
32._at:  troops = 1.55
33._at:  troops =  1.6
34._at:  troops = 1.65
35._at:  troops =  1.7
36._at:  troops = 1.75
37._at:  troops =  1.8
38._at:  troops = 1.85
39._at:  troops =  1.9
40._at:  troops = 1.95
41._at:  troops =    2
42._at:  troops = 2.05
43._at:  troops =  2.1
44._at:  troops = 2.15
45._at:  troops =  2.2
46._at:  troops = 2.25
47._at:  troops =  2.3
48._at:  troops = 2.35
49._at:  troops =  2.4
50._at:  troops = 2.45
51._at:  troops =  2.5
52._at:  troops = 2.55
53._at:  troops =  2.6
54._at:  troops = 2.65
55._at:  troops =  2.7
56._at:  troops = 2.75
57._at:  troops =  2.8
58._at:  troops = 2.85
59._at:  troops =  2.9
60._at:  troops = 2.95
61._at:  troops =    3
62._at:  troops = 3.05
63._at:  troops =  3.1
64._at:  troops = 3.15
65._at:  troops =  3.2
66._at:  troops = 3.25
67._at:  troops =  3.3
68._at:  troops = 3.35
69._at:  troops =  3.4
70._at:  troops = 3.45
71._at:  troops =  3.5
72._at:  troops = 3.55
73._at:  troops =  3.6
74._at:  troops = 3.65
75._at:  troops =  3.7
76._at:  troops = 3.75
77._at:  troops =  3.8
78._at:  troops = 3.85
79._at:  troops =  3.9
80._at:  troops = 3.95
81._at:  troops =    4
82._at:  troops = 4.05
83._at:  troops =  4.1
84._at:  troops = 4.15
85._at:  troops =  4.2
86._at:  troops = 4.25
87._at:  troops =  4.3
88._at:  troops = 4.35
89._at:  troops =  4.4
90._at:  troops = 4.45
91._at:  troops =  4.5
92._at:  troops = 4.55
93._at:  troops =  4.6
94._at:  troops = 4.65
95._at:  troops =  4.7
96._at:  troops = 4.75
97._at:  troops =  4.8
98._at:  troops = 4.85
99._at:  troops =  4.9
100._at: troops = 4.95
101._at: troops =    5
102._at: troops = 5.05
103._at: troops =  5.1
104._at: troops = 5.15
105._at: troops =  5.2
106._at: troops = 5.25
107._at: troops =  5.3
108._at: troops = 5.35
109._at: troops =  5.4
110._at: troops = 5.45
111._at: troops =  5.5
112._at: troops = 5.55
113._at: troops =  5.6
114._at: troops = 5.65
115._at: troops =  5.7
116._at: troops = 5.75
117._at: troops =  5.8
118._at: troops = 5.85
119._at: troops =  5.9
120._at: troops = 5.95
121._at: troops =    6
122._at: troops = 6.05
123._at: troops =  6.1
124._at: troops = 6.15
125._at: troops =  6.2
126._at: troops = 6.25
127._at: troops =  6.3
128._at: troops = 6.35
129._at: troops =  6.4
130._at: troops = 6.45
131._at: troops =  6.5
132._at: troops = 6.55
133._at: troops =  6.6
134._at: troops = 6.65
135._at: troops =  6.7
136._at: troops = 6.75
137._at: troops =  6.8
138._at: troops = 6.85
139._at: troops =  6.9
140._at: troops = 6.95
141._at: troops =    7
142._at: troops = 7.05
143._at: troops =  7.1
144._at: troops = 7.15
145._at: troops =  7.2
146._at: troops = 7.25
147._at: troops =  7.3
148._at: troops = 7.35
149._at: troops =  7.4
150._at: troops = 7.45
151._at: troops =  7.5
152._at: troops = 7.55
153._at: troops =  7.6
154._at: troops = 7.65
155._at: troops =  7.7
156._at: troops = 7.75
157._at: troops =  7.8
158._at: troops = 7.85
159._at: troops =  7.9
160._at: troops = 7.95
161._at: troops =    8
162._at: troops = 8.05
163._at: troops =  8.1
164._at: troops = 8.15
165._at: troops =  8.2
166._at: troops = 8.25
167._at: troops =  8.3
168._at: troops = 8.35
169._at: troops =  8.4
170._at: troops = 8.45
171._at: troops =  8.5
172._at: troops = 8.55
173._at: troops =  8.6
174._at: troops = 8.65
175._at: troops =  8.7
176._at: troops = 8.75
177._at: troops =  8.8
178._at: troops = 8.85
179._at: troops =  8.9
180._at: troops = 8.95
181._at: troops =    9
182._at: troops = 9.05
183._at: troops =  9.1
184._at: troops = 9.15
185._at: troops =  9.2
186._at: troops = 9.25
187._at: troops =  9.3
188._at: troops = 9.35
189._at: troops =  9.4
190._at: troops = 9.45
191._at: troops =  9.5
192._at: troops = 9.55
193._at: troops =  9.6
194._at: troops = 9.65
195._at: troops =  9.7
196._at: troops = 9.75
197._at: troops =  9.8

-------------------------------------------------------------------------------------
                    |            Delta-method
                    |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
--------------------+----------------------------------------------------------------
0.robust_anocracy5  |  (base outcome)
--------------------+----------------------------------------------------------------
1.robust_anocracy5  |
                _at |
                 1  |   .0262459   .0113033     2.32   0.020     .0040918    .0483999
                 2  |   .0193576   .0099818     1.94   0.052    -.0002065    .0389216
                 3  |   .0133544   .0090922     1.47   0.142     -.004466    .0311748
                 4  |   .0081223   .0084958     0.96   0.339    -.0085292    .0247737
                 5  |   .0035618   .0080846     0.44   0.660    -.0122837    .0194074
                 6  |  -.0004135   .0077838    -0.05   0.958    -.0156696    .0148425
                 7  |  -.0038792   .0075471    -0.51   0.607    -.0186712    .0109128
                 8  |  -.0069009   .0073487    -0.94   0.348    -.0213041    .0075024
                 9  |  -.0095358   .0071768    -1.33   0.184     -.023602    .0045305
                10  |  -.0118337   .0070274    -1.68   0.092    -.0256072    .0019398
                11  |  -.0138382   .0069011    -2.01   0.045     -.027364   -.0003123
                12  |  -.0155869   .0068001    -2.29   0.022    -.0289148   -.0022591
                13  |   -.017113   .0067271    -2.54   0.011    -.0302978   -.0039282
                14  |   -.018445   .0066843    -2.76   0.006    -.0315461    -.005344
                15  |  -.0196081   .0066731    -2.94   0.003    -.0326872    -.006529
                16  |  -.0206239   .0066936    -3.08   0.002    -.0337431   -.0075047
                17  |  -.0215114    .006745    -3.19   0.001    -.0347314   -.0082915
                18  |  -.0222873   .0068256    -3.27   0.001    -.0356652   -.0089094
                19  |  -.0229658   .0069332    -3.31   0.001    -.0365547    -.009377
                20  |  -.0235596   .0070652    -3.33   0.001    -.0374072   -.0097119
                21  |  -.0240795    .007219    -3.34   0.001    -.0382284   -.0099306
                22  |   -.024535   .0073917    -3.32   0.001    -.0390224   -.0100476
                23  |  -.0249346   .0075808    -3.29   0.001    -.0397928   -.0100764
                24  |  -.0252853   .0077842    -3.25   0.001     -.040542   -.0100286
                25  |  -.0255935   .0079996    -3.20   0.001    -.0412725   -.0099146
                26  |  -.0258647   .0082254    -3.14   0.002    -.0419862   -.0097432
                27  |  -.0261036     .00846    -3.09   0.002    -.0426849   -.0095223
                28  |  -.0263144   .0087022    -3.02   0.002    -.0433704   -.0092584
                29  |  -.0265008   .0089509    -2.96   0.003    -.0440442   -.0089573
                30  |  -.0266658   .0092052    -2.90   0.004    -.0447077   -.0086239
                31  |  -.0268122   .0094644    -2.83   0.005    -.0453622   -.0082623
                32  |  -.0269425    .009728    -2.77   0.006     -.046009    -.007876
                33  |  -.0270587   .0099954    -2.71   0.007    -.0466493    -.007468
                34  |  -.0271626   .0102663    -2.65   0.008    -.0472841   -.0070411
                35  |  -.0272558   .0105402    -2.59   0.010    -.0479143   -.0065973
                36  |  -.0273397   .0108171    -2.53   0.011    -.0485408   -.0061387
                37  |  -.0274155   .0110965    -2.47   0.013    -.0491643   -.0056667
                38  |  -.0274843   .0113785    -2.42   0.016    -.0497857   -.0051829
                39  |  -.0275469   .0116627    -2.36   0.018    -.0504053   -.0046884
                40  |  -.0276041   .0119491    -2.31   0.021    -.0510239   -.0041843
                41  |  -.0276567   .0122376    -2.26   0.024    -.0516419   -.0036715
                42  |  -.0277052    .012528    -2.21   0.027    -.0522597   -.0031508
                43  |  -.0277502   .0128203    -2.16   0.030    -.0528776   -.0026229
                44  |  -.0277921   .0131144    -2.12   0.034    -.0534959   -.0020883
                45  |  -.0278314   .0134103    -2.08   0.038     -.054115   -.0015477
                46  |  -.0278683   .0137078    -2.03   0.042     -.054735   -.0010015
                47  |  -.0279032   .0140069    -1.99   0.046    -.0553562   -.0004502
                48  |  -.0279363   .0143076    -1.95   0.051    -.0559786    .0001061
                49  |  -.0279679   .0146098    -1.91   0.056    -.0566025    .0006668
                50  |  -.0279981   .0149134    -1.88   0.060    -.0572279    .0012317
                51  |  -.0280272   .0152185    -1.84   0.066    -.0578549    .0018005
                52  |  -.0280553   .0155249    -1.81   0.071    -.0584836    .0023731
                53  |  -.0280824   .0158327    -1.77   0.076     -.059114    .0029491
                54  |  -.0281089   .0161418    -1.74   0.082    -.0597463    .0035285
                55  |  -.0281347   .0164522    -1.71   0.087    -.0603804     .004111
                56  |  -.0281599   .0167638    -1.68   0.093    -.0610163    .0046966
                57  |  -.0281846   .0170766    -1.65   0.099     -.061654    .0052849
                58  |  -.0282088   .0173905    -1.62   0.105    -.0622937     .005876
                59  |  -.0282327   .0177057    -1.59   0.111    -.0629352    .0064697
                60  |  -.0282563   .0180219    -1.57   0.117    -.0635785     .007066
                61  |  -.0282796   .0183392    -1.54   0.123    -.0642237    .0076646
                62  |  -.0283026   .0186576    -1.52   0.129    -.0648708    .0082655
                63  |  -.0283255    .018977    -1.49   0.136    -.0655197    .0088688
                64  |  -.0283481   .0192974    -1.47   0.142    -.0661704    .0094741
                65  |  -.0283706   .0196189    -1.45   0.148    -.0668229    .0100816
                66  |   -.028393   .0199413    -1.42   0.154    -.0674772    .0106912
                67  |  -.0284153   .0202646    -1.40   0.161    -.0681332    .0113027
                68  |  -.0284374   .0205889    -1.38   0.167     -.068791    .0119161
                69  |  -.0284595   .0209142    -1.36   0.174    -.0694505    .0125315
                70  |  -.0284815   .0212403    -1.34   0.180    -.0701117    .0131487
                71  |  -.0285035   .0215673    -1.32   0.186    -.0707746    .0137677
                72  |  -.0285254   .0218952    -1.30   0.193    -.0714391    .0143884
                73  |  -.0285472   .0222239    -1.28   0.199    -.0721053    .0150109
                74  |  -.0285691   .0225535    -1.27   0.205    -.0727731     .015635
                75  |  -.0285909   .0228839    -1.25   0.212    -.0734426    .0162608
                76  |  -.0286126   .0232152    -1.23   0.218    -.0741136    .0168883
                77  |  -.0286344   .0235472    -1.22   0.224    -.0747862    .0175173
                78  |  -.0286562   .0238801    -1.20   0.230    -.0754603     .018148
                79  |  -.0286779   .0242137    -1.18   0.236    -.0761359    .0187801
                80  |  -.0286996   .0245481    -1.17   0.242    -.0768131    .0194138
                81  |  -.0287214   .0248833    -1.15   0.248    -.0774918     .020049
                82  |  -.0287431   .0252193    -1.14   0.254     -.078172    .0206857
                83  |  -.0287649    .025556    -1.13   0.260    -.0788536    .0213239
                84  |  -.0287866   .0258934    -1.11   0.266    -.0795367    .0219635
                85  |  -.0288084   .0262315    -1.10   0.272    -.0802212    .0226045
                86  |  -.0288301   .0265704    -1.09   0.278    -.0809072     .023247
                87  |  -.0288519   .0269101    -1.07   0.284    -.0815946    .0238909
                88  |  -.0288736   .0272504    -1.06   0.289    -.0822834    .0245361
                89  |  -.0288954   .0275914    -1.05   0.295    -.0829736    .0251827
                90  |  -.0289172   .0279331    -1.04   0.301    -.0836651    .0258307
                91  |   -.028939   .0282755    -1.02   0.306    -.0843581      .02648
                92  |  -.0289608   .0286187    -1.01   0.312    -.0850524    .0271307
                93  |  -.0289827   .0289624    -1.00   0.317     -.085748    .0277827
                94  |  -.0290045   .0293069    -0.99   0.322     -.086445     .028436
                95  |  -.0290264   .0296521    -0.98   0.328    -.0871434    .0290906
                96  |  -.0290482   .0299979    -0.97   0.333     -.087843    .0297465
                97  |  -.0290701   .0303444    -0.96   0.338     -.088544    .0304037
                98  |   -.029092   .0306915    -0.95   0.343    -.0892463    .0310622
                99  |  -.0291139   .0310393    -0.94   0.348    -.0899498    .0317219
               100  |  -.0291359   .0313877    -0.93   0.353    -.0906547     .032383
               101  |  -.0291578   .0317368    -0.92   0.358    -.0913609    .0330452
               102  |  -.0291798   .0320866    -0.91   0.363    -.0920683    .0337088
               103  |  -.0292018    .032437    -0.90   0.368    -.0927771    .0343735
               104  |  -.0292238    .032788    -0.89   0.373     -.093487    .0350395
               105  |  -.0292458   .0331397    -0.88   0.378    -.0941983    .0357068
               106  |  -.0292678   .0334919    -0.87   0.382    -.0949108    .0363752
               107  |  -.0292898   .0338449    -0.87   0.387    -.0956246    .0370449
               108  |  -.0293119   .0341984    -0.86   0.391    -.0963396    .0377158
               109  |   -.029334   .0345526    -0.85   0.396    -.0970558    .0383879
               110  |  -.0293561   .0349074    -0.84   0.400    -.0977733    .0390612
               111  |  -.0293782   .0352628    -0.83   0.405     -.098492    .0397357
               112  |  -.0294003   .0356189    -0.83   0.409     -.099212    .0404114
               113  |  -.0294224   .0359755    -0.82   0.413    -.0999332    .0410883
               114  |  -.0294446   .0363328    -0.81   0.418    -.1006556    .0417664
               115  |  -.0294667   .0366907    -0.80   0.422    -.1013792    .0424457
               116  |  -.0294889   .0370492    -0.80   0.426     -.102104    .0431261
               117  |  -.0295111   .0374083    -0.79   0.430    -.1028301    .0438078
               118  |  -.0295334    .037768    -0.78   0.434    -.1035573    .0444906
               119  |  -.0295556   .0381283    -0.78   0.438    -.1042858    .0451746
               120  |  -.0295779   .0384893    -0.77   0.442    -.1050154    .0458597
               121  |  -.0296001   .0388508    -0.76   0.446    -.1057463     .046546
               122  |  -.0296224   .0392129    -0.76   0.450    -.1064784    .0472335
               123  |  -.0296447   .0395757    -0.75   0.454    -.1072116    .0479222
               124  |   -.029667    .039939    -0.74   0.458     -.107946     .048612
               125  |  -.0296894   .0403029    -0.74   0.461    -.1086817    .0493029
               126  |  -.0297117   .0406675    -0.73   0.465    -.1094185     .049995
               127  |  -.0297341   .0410326    -0.72   0.469    -.1101565    .0506883
               128  |  -.0297565   .0413983    -0.72   0.472    -.1108957    .0513827
               129  |  -.0297789   .0417646    -0.71   0.476    -.1116361    .0520783
               130  |  -.0298013   .0421316    -0.71   0.479    -.1123777     .052775
               131  |  -.0298238   .0424991    -0.70   0.483    -.1131204    .0534729
               132  |  -.0298462   .0428672    -0.70   0.486    -.1138643    .0541719
               133  |  -.0298687   .0432359    -0.69   0.490    -.1146094     .054872
               134  |  -.0298912   .0436051    -0.69   0.493    -.1153557    .0555733
               135  |  -.0299137    .043975    -0.68   0.496    -.1161031    .0562757
               136  |  -.0299362   .0443455    -0.68   0.500    -.1168517    .0569793
               137  |  -.0299588   .0447165    -0.67   0.503    -.1176015     .057684
               138  |  -.0299813   .0450882    -0.66   0.506    -.1183525    .0583899
               139  |  -.0300039   .0454604    -0.66   0.509    -.1191046    .0590968
               140  |  -.0300265   .0458332    -0.66   0.512    -.1198579    .0598049
               141  |  -.0300491   .0462066    -0.65   0.515    -.1206124    .0605142
               142  |  -.0300717   .0465806    -0.65   0.519     -.121368    .0612246
               143  |  -.0300944   .0469552    -0.64   0.522    -.1221248    .0619361
               144  |   -.030117   .0473303    -0.64   0.525    -.1228827    .0626487
               145  |  -.0301397   .0477061    -0.63   0.528    -.1236419    .0633625
               146  |  -.0301624   .0480824    -0.63   0.530    -.1244022    .0640774
               147  |  -.0301851   .0484593    -0.62   0.533    -.1251636    .0647934
               148  |  -.0302078   .0488368    -0.62   0.536    -.1259262    .0655106
               149  |  -.0302306   .0492149    -0.61   0.539      -.12669    .0662289
               150  |  -.0302533   .0495936    -0.61   0.542     -.127455    .0669483
               151  |  -.0302761   .0499728    -0.61   0.545    -.1282211    .0676688
               152  |  -.0302989   .0503527    -0.60   0.547    -.1289883    .0683905
               153  |  -.0303217   .0507331    -0.60   0.550    -.1297568    .0691133
               154  |  -.0303446   .0511141    -0.59   0.553    -.1305263    .0698372
               155  |  -.0303674   .0514957    -0.59   0.555    -.1312971    .0705623
               156  |  -.0303903   .0518779    -0.59   0.558     -.132069    .0712885
               157  |  -.0304131   .0522606    -0.58   0.561    -.1328421    .0720158
               158  |   -.030436    .052644    -0.58   0.563    -.1336163    .0727442
               159  |   -.030459   .0530279    -0.57   0.566    -.1343917    .0734738
               160  |  -.0304819   .0534124    -0.57   0.568    -.1351683    .0742045
               161  |  -.0305049   .0537975    -0.57   0.571     -.135946    .0749363
               162  |  -.0305278   .0541832    -0.56   0.573    -.1367249    .0756692
               163  |  -.0305508   .0545694    -0.56   0.576    -.1375049    .0764033
               164  |  -.0305738   .0549563    -0.56   0.578    -.1382861    .0771385
               165  |  -.0305968   .0553437    -0.55   0.580    -.1390685    .0778748
               166  |  -.0306199   .0557317    -0.55   0.583     -.139852    .0786123
               167  |  -.0306429   .0561203    -0.55   0.585    -.1406367    .0793508
               168  |   -.030666   .0565095    -0.54   0.587    -.1414225    .0800905
               169  |  -.0306891   .0568992    -0.54   0.590    -.1422095    .0808314
               170  |  -.0307122   .0572896    -0.54   0.592    -.1429977    .0815733
               171  |  -.0307353   .0576805    -0.53   0.594     -.143787    .0823164
               172  |  -.0307585    .058072    -0.53   0.596    -.1445775    .0830606
               173  |  -.0307816   .0584641    -0.53   0.599    -.1453692     .083806
               174  |  -.0308048   .0588568    -0.52   0.601     -.146162    .0845524
               175  |   -.030828   .0592501    -0.52   0.603     -.146956       .0853
               176  |  -.0308512   .0596439    -0.52   0.605    -.1477512    .0860488
               177  |  -.0308744   .0600384    -0.51   0.607    -.1485475    .0867986
               178  |  -.0308977   .0604334    -0.51   0.609     -.149345    .0875496
               179  |  -.0309209    .060829    -0.51   0.611    -.1501436    .0883017
               180  |  -.0309442   .0612252    -0.51   0.613    -.1509434     .089055
               181  |  -.0309675    .061622    -0.50   0.615    -.1517444    .0898094
               182  |  -.0309908   .0620194    -0.50   0.617    -.1525466    .0905649
               183  |  -.0310142   .0624173    -0.50   0.619    -.1533499    .0913215
               184  |  -.0310375   .0628159    -0.49   0.621    -.1541544    .0920793
               185  |  -.0310609    .063215    -0.49   0.623      -.15496    .0928382
               186  |  -.0310843   .0636147    -0.49   0.625    -.1557669    .0935983
               187  |  -.0311077    .064015    -0.49   0.627    -.1565749    .0943595
               188  |  -.0311311   .0644159    -0.48   0.629     -.157384    .0951218
               189  |  -.0311546   .0648174    -0.48   0.631    -.1581944    .0958852
               190  |   -.031178   .0652195    -0.48   0.633    -.1590059    .0966498
               191  |  -.0312015   .0656222    -0.48   0.634    -.1598186    .0974156
               192  |   -.031225   .0660254    -0.47   0.636    -.1606324    .0981825
               193  |  -.0312485   .0664293    -0.47   0.638    -.1614475    .0989505
               194  |   -.031272   .0668337    -0.47   0.640    -.1622637    .0997196
               195  |  -.0312956   .0672387    -0.47   0.642    -.1630811    .1004899
               196  |  -.0313191   .0676444    -0.46   0.643    -.1638996    .1012614
               197  |  -.0313427   .0680506    -0.46   0.645    -.1647194    .1020339
-------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. marginsplot, ///
>         recast(line) plot1opts(lcolor(gs8)) ciopt(color(black%20)) recastci(rarea) ///
>         ytitle("AME", size(medsmall)) ///
>         xtitle("Troops", size(medsmall))  ///
>         title("Anocracy", size(medium)) yline(0, lcolor(red)) ///
>         addplot(hist troops, /// adding histogram marginsplot graph
> blcolor(black%30) fcolor(white%10) /// bar line and fill colors
> percent /// histogram bins in "percent" rather than "discrete" (actual)
> yaxis(2) /// calls for 2nd y-axis
> yscale(alt lcolor() axis(2)) /// scaling on 2nd y-axis
> ylabel(0 "0%" 20 "20%" 40 "40%" 60 "60%" 80 "80%" 100 "100%", /// labels on 2nd y-axis
> labcolor() axis(2) tlcolor(black) tlwidth(thin) labsize(small)) /// label options on 2nd y-axis
> ytitle(" ", axis(2)))  legend(off)

Variables that uniquely identify margins: troops

. graph save "figures\Study_2\B2.4.gph", replace
file figures\Study_2\B2.4.gph saved

. 
. 
. *model 8a: New democracy
. eststo: poisson milcoupsum new_robust_democracy6##c.troops population2000 gdpcap durable conflict milexp exsol, cluster(ccode)

Iteration 0:   log pseudolikelihood = -411.78171  
Iteration 1:   log pseudolikelihood = -406.87226  
Iteration 2:   log pseudolikelihood = -403.34415  
Iteration 3:   log pseudolikelihood = -400.72867  
Iteration 4:   log pseudolikelihood = -400.69906  
Iteration 5:   log pseudolikelihood = -400.69903  

Poisson regression                                      Number of obs =  3,001
                                                        Wald chi2(9)  =  72.73
Log pseudolikelihood = -400.69903                       Prob > chi2   = 0.0000

                                                  (Std. err. adjusted for 148 clusters in ccode)
------------------------------------------------------------------------------------------------
                               |               Robust
                    milcoupsum | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------------------------+----------------------------------------------------------------
       1.new_robust_democracy6 |  -1.118677   .4094854    -2.73   0.006    -1.921254   -.3161008
                        troops |  -.1720984   .3543506    -0.49   0.627    -.8666128    .5224159
                               |
new_robust_democracy6#c.troops |
                            1  |  -.6602458   .7434278    -0.89   0.374    -2.117338     .796846
                               |
                population2000 |  -3.33e-10   6.06e-09    -0.05   0.956    -1.22e-08    1.15e-08
                        gdpcap |   -.007618   .0313899    -0.24   0.808    -.0691412    .0539051
                       durable |  -.0167517    .021227    -0.79   0.430    -.0583558    .0248523
                      conflict |   .8082747   .3447763     2.34   0.019     .1325255    1.484024
                        milexp |  -398.4421   171.2426    -2.33   0.020    -734.0715   -62.81271
                         exsol |   .7746237   2.123611     0.36   0.715    -3.387578    4.936825
                         _cons |  -2.430278    .319332    -7.61   0.000    -3.056157   -1.804399
------------------------------------------------------------------------------------------------
(est11 stored)

. margins, dydx(new_robust_democracy6) at (troops =  (0(0.05)9.8)) post

Average marginal effects                                 Number of obs = 3,001
Model VCE: Robust

Expression: Predicted number of events, predict()
dy/dx wrt:  1.new_robust_democracy6
1._at:   troops =    0
2._at:   troops =  .05
3._at:   troops =   .1
4._at:   troops =  .15
5._at:   troops =   .2
6._at:   troops =  .25
7._at:   troops =   .3
8._at:   troops =  .35
9._at:   troops =   .4
10._at:  troops =  .45
11._at:  troops =   .5
12._at:  troops =  .55
13._at:  troops =   .6
14._at:  troops =  .65
15._at:  troops =   .7
16._at:  troops =  .75
17._at:  troops =   .8
18._at:  troops =  .85
19._at:  troops =   .9
20._at:  troops =  .95
21._at:  troops =    1
22._at:  troops = 1.05
23._at:  troops =  1.1
24._at:  troops = 1.15
25._at:  troops =  1.2
26._at:  troops = 1.25
27._at:  troops =  1.3
28._at:  troops = 1.35
29._at:  troops =  1.4
30._at:  troops = 1.45
31._at:  troops =  1.5
32._at:  troops = 1.55
33._at:  troops =  1.6
34._at:  troops = 1.65
35._at:  troops =  1.7
36._at:  troops = 1.75
37._at:  troops =  1.8
38._at:  troops = 1.85
39._at:  troops =  1.9
40._at:  troops = 1.95
41._at:  troops =    2
42._at:  troops = 2.05
43._at:  troops =  2.1
44._at:  troops = 2.15
45._at:  troops =  2.2
46._at:  troops = 2.25
47._at:  troops =  2.3
48._at:  troops = 2.35
49._at:  troops =  2.4
50._at:  troops = 2.45
51._at:  troops =  2.5
52._at:  troops = 2.55
53._at:  troops =  2.6
54._at:  troops = 2.65
55._at:  troops =  2.7
56._at:  troops = 2.75
57._at:  troops =  2.8
58._at:  troops = 2.85
59._at:  troops =  2.9
60._at:  troops = 2.95
61._at:  troops =    3
62._at:  troops = 3.05
63._at:  troops =  3.1
64._at:  troops = 3.15
65._at:  troops =  3.2
66._at:  troops = 3.25
67._at:  troops =  3.3
68._at:  troops = 3.35
69._at:  troops =  3.4
70._at:  troops = 3.45
71._at:  troops =  3.5
72._at:  troops = 3.55
73._at:  troops =  3.6
74._at:  troops = 3.65
75._at:  troops =  3.7
76._at:  troops = 3.75
77._at:  troops =  3.8
78._at:  troops = 3.85
79._at:  troops =  3.9
80._at:  troops = 3.95
81._at:  troops =    4
82._at:  troops = 4.05
83._at:  troops =  4.1
84._at:  troops = 4.15
85._at:  troops =  4.2
86._at:  troops = 4.25
87._at:  troops =  4.3
88._at:  troops = 4.35
89._at:  troops =  4.4
90._at:  troops = 4.45
91._at:  troops =  4.5
92._at:  troops = 4.55
93._at:  troops =  4.6
94._at:  troops = 4.65
95._at:  troops =  4.7
96._at:  troops = 4.75
97._at:  troops =  4.8
98._at:  troops = 4.85
99._at:  troops =  4.9
100._at: troops = 4.95
101._at: troops =    5
102._at: troops = 5.05
103._at: troops =  5.1
104._at: troops = 5.15
105._at: troops =  5.2
106._at: troops = 5.25
107._at: troops =  5.3
108._at: troops = 5.35
109._at: troops =  5.4
110._at: troops = 5.45
111._at: troops =  5.5
112._at: troops = 5.55
113._at: troops =  5.6
114._at: troops = 5.65
115._at: troops =  5.7
116._at: troops = 5.75
117._at: troops =  5.8
118._at: troops = 5.85
119._at: troops =  5.9
120._at: troops = 5.95
121._at: troops =    6
122._at: troops = 6.05
123._at: troops =  6.1
124._at: troops = 6.15
125._at: troops =  6.2
126._at: troops = 6.25
127._at: troops =  6.3
128._at: troops = 6.35
129._at: troops =  6.4
130._at: troops = 6.45
131._at: troops =  6.5
132._at: troops = 6.55
133._at: troops =  6.6
134._at: troops = 6.65
135._at: troops =  6.7
136._at: troops = 6.75
137._at: troops =  6.8
138._at: troops = 6.85
139._at: troops =  6.9
140._at: troops = 6.95
141._at: troops =    7
142._at: troops = 7.05
143._at: troops =  7.1
144._at: troops = 7.15
145._at: troops =  7.2
146._at: troops = 7.25
147._at: troops =  7.3
148._at: troops = 7.35
149._at: troops =  7.4
150._at: troops = 7.45
151._at: troops =  7.5
152._at: troops = 7.55
153._at: troops =  7.6
154._at: troops = 7.65
155._at: troops =  7.7
156._at: troops = 7.75
157._at: troops =  7.8
158._at: troops = 7.85
159._at: troops =  7.9
160._at: troops = 7.95
161._at: troops =    8
162._at: troops = 8.05
163._at: troops =  8.1
164._at: troops = 8.15
165._at: troops =  8.2
166._at: troops = 8.25
167._at: troops =  8.3
168._at: troops = 8.35
169._at: troops =  8.4
170._at: troops = 8.45
171._at: troops =  8.5
172._at: troops = 8.55
173._at: troops =  8.6
174._at: troops = 8.65
175._at: troops =  8.7
176._at: troops = 8.75
177._at: troops =  8.8
178._at: troops = 8.85
179._at: troops =  8.9
180._at: troops = 8.95
181._at: troops =    9
182._at: troops = 9.05
183._at: troops =  9.1
184._at: troops = 9.15
185._at: troops =  9.2
186._at: troops = 9.25
187._at: troops =  9.3
188._at: troops = 9.35
189._at: troops =  9.4
190._at: troops = 9.45
191._at: troops =  9.5
192._at: troops = 9.55
193._at: troops =  9.6
194._at: troops = 9.65
195._at: troops =  9.7
196._at: troops = 9.75
197._at: troops =  9.8

------------------------------------------------------------------------------------------
                         |            Delta-method
                         |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------------------+----------------------------------------------------------------
0.new_robust_democracy6  |  (base outcome)
-------------------------+----------------------------------------------------------------
1.new_robust_democracy6  |
                     _at |
                      1  |  -.0334746   .0101015    -3.31   0.001    -.0532733    -.013676
                      2  |  -.0337108   .0096425    -3.50   0.000    -.0526098   -.0148118
                      3  |  -.0339236   .0092188    -3.68   0.000    -.0519921   -.0158551
                      4  |  -.0341141   .0088303    -3.86   0.000    -.0514212    -.016807
                      5  |  -.0342834   .0084779    -4.04   0.000    -.0508997   -.0176671
                      6  |  -.0344325    .008163    -4.22   0.000    -.0504317   -.0184332
                      7  |  -.0345622    .007888    -4.38   0.000    -.0500223    -.019102
                      8  |  -.0346734   .0076553    -4.53   0.000    -.0496776   -.0196692
                      9  |  -.0347671    .007468    -4.66   0.000    -.0494042   -.0201301
                     10  |  -.0348441   .0073288    -4.75   0.000    -.0492083   -.0204799
                     11  |  -.0349052   .0072401    -4.82   0.000    -.0490956   -.0207148
                     12  |   -.034951   .0072037    -4.85   0.000    -.0490701   -.0208319
                     13  |  -.0349824   .0072204    -4.84   0.000    -.0491342   -.0208306
                     14  |  -.0350001   .0072899    -4.80   0.000    -.0492881    -.020712
                     15  |  -.0350046   .0074109    -4.72   0.000    -.0495297   -.0204796
                     16  |  -.0349968   .0075809    -4.62   0.000     -.049855   -.0201385
                     17  |   -.034977   .0077967    -4.49   0.000    -.0502582   -.0196958
                     18  |  -.0349461   .0080546    -4.34   0.000    -.0507327   -.0191594
                     19  |  -.0349044   .0083505    -4.18   0.000     -.051271   -.0185378
                     20  |  -.0348526   .0086802    -4.02   0.000    -.0518655   -.0178397
                     21  |  -.0347911   .0090397    -3.85   0.000    -.0525087   -.0170736
                     22  |  -.0347205   .0094251    -3.68   0.000    -.0531935   -.0162476
                     23  |  -.0346413   .0098329    -3.52   0.000    -.0539134   -.0153692
                     24  |  -.0345538   .0102597    -3.37   0.001    -.0546625   -.0144452
                     25  |  -.0344586   .0107026    -3.22   0.001    -.0554352   -.0134819
                     26  |  -.0343559   .0111589    -3.08   0.002     -.056227   -.0124849
                     27  |  -.0342463   .0116263    -2.95   0.003    -.0570334   -.0114592
                     28  |  -.0341301   .0121026    -2.82   0.005    -.0578509   -.0104094
                     29  |  -.0340077   .0125861    -2.70   0.007     -.058676   -.0093394
                     30  |  -.0338794    .013075    -2.59   0.010    -.0595059   -.0082529
                     31  |  -.0337456   .0135679    -2.49   0.013    -.0603381    -.007153
                     32  |  -.0336065   .0140635    -2.39   0.017    -.0611704   -.0060426
                     33  |  -.0334625   .0145606    -2.30   0.022    -.0620007   -.0049243
                     34  |  -.0333139   .0150581    -2.21   0.027    -.0628273   -.0038005
                     35  |  -.0331609   .0155553    -2.13   0.033    -.0636487   -.0026731
                     36  |  -.0330038   .0160512    -2.06   0.040    -.0644635   -.0015441
                     37  |  -.0328429   .0165451    -1.99   0.047    -.0652706   -.0004151
                     38  |  -.0326784   .0170363    -1.92   0.055     -.066069    .0007122
                     39  |  -.0325105   .0175244    -1.86   0.064    -.0668576    .0018366
                     40  |  -.0323395   .0180086    -1.80   0.073    -.0676357    .0029567
                     41  |  -.0321656   .0184886    -1.74   0.082    -.0684027    .0040714
                     42  |   -.031989    .018964    -1.69   0.092    -.0691577    .0051797
                     43  |  -.0318098   .0194343    -1.64   0.102    -.0699004    .0062807
                     44  |  -.0316284   .0198992    -1.59   0.112    -.0706302    .0073734
                     45  |  -.0314447   .0203585    -1.54   0.122    -.0713467    .0084572
                     46  |  -.0312591   .0208118    -1.50   0.133    -.0720495    .0095313
                     47  |  -.0310716    .021259    -1.46   0.144    -.0727384    .0105951
                     48  |  -.0308825   .0216997    -1.42   0.155    -.0734132    .0116481
                     49  |  -.0306919   .0221339    -1.39   0.166    -.0740734    .0126897
                     50  |  -.0304999   .0225613    -1.35   0.176    -.0747191    .0137194
                     51  |  -.0303066   .0229818    -1.32   0.187    -.0753501    .0147369
                     52  |  -.0301122   .0233953    -1.29   0.198    -.0759662    .0157418
                     53  |  -.0299168   .0238018    -1.26   0.209    -.0765674    .0167338
                     54  |  -.0297205    .024201    -1.23   0.219    -.0771536    .0177125
                     55  |  -.0295235    .024593    -1.20   0.230    -.0777248    .0186778
                     56  |  -.0293258   .0249776    -1.17   0.240     -.078281    .0196294
                     57  |  -.0291276   .0253549    -1.15   0.251    -.0788222     .020567
                     58  |  -.0289289   .0257247    -1.12   0.261    -.0793484    .0214906
                     59  |  -.0287298   .0260871    -1.10   0.271    -.0798597    .0224001
                     60  |  -.0285304   .0264421    -1.08   0.281    -.0803561    .0232952
                     61  |  -.0283309   .0267897    -1.06   0.290    -.0808377     .024176
                     62  |  -.0281312   .0271298    -1.04   0.300    -.0813046    .0250423
                     63  |  -.0279314   .0274625    -1.02   0.309    -.0817569     .025894
                     64  |  -.0277317   .0277878    -1.00   0.318    -.0821948    .0267313
                     65  |  -.0275321   .0281057    -0.98   0.327    -.0826182     .027554
                     66  |  -.0273327   .0284162    -0.96   0.336    -.0830275    .0283621
                     67  |  -.0271335   .0287195    -0.94   0.345    -.0834226    .0291557
                     68  |  -.0269345   .0290155    -0.93   0.353    -.0838038    .0299347
                     69  |  -.0267359   .0293042    -0.91   0.362    -.0841712    .0306993
                     70  |  -.0265377   .0295858    -0.90   0.370    -.0845249    .0314495
                     71  |    -.02634   .0298603    -0.88   0.378    -.0848652    .0321853
                     72  |  -.0261427   .0301278    -0.87   0.386    -.0851921    .0329067
                     73  |  -.0259459   .0303883    -0.85   0.393    -.0855058     .033614
                     74  |  -.0257497   .0306418    -0.84   0.401    -.0858066    .0343072
                     75  |  -.0255542   .0308885    -0.83   0.408    -.0860946    .0349863
                     76  |  -.0253593   .0311285    -0.81   0.415      -.08637    .0356514
                     77  |  -.0251651   .0313617    -0.80   0.422    -.0866329    .0363027
                     78  |  -.0249716   .0315883    -0.79   0.429    -.0868835    .0369404
                     79  |  -.0247788   .0318083    -0.78   0.436     -.087122    .0375644
                     80  |  -.0245869   .0320219    -0.77   0.443    -.0873487    .0381749
                     81  |  -.0243957   .0322291    -0.76   0.449    -.0875636    .0387721
                     82  |  -.0242055   .0324299    -0.75   0.455     -.087767    .0393561
                     83  |   -.024016   .0326246    -0.74   0.462     -.087959    .0399269
                     84  |  -.0238275    .032813    -0.73   0.468    -.0881399    .0404849
                     85  |  -.0236399   .0329954    -0.72   0.474    -.0883098      .04103
                     86  |  -.0234532   .0331719    -0.71   0.480    -.0884688    .0415625
                     87  |  -.0232674   .0333424    -0.70   0.485    -.0886173    .0420824
                     88  |  -.0230827   .0335071    -0.69   0.491    -.0887554      .04259
                     89  |  -.0228989    .033666    -0.68   0.496    -.0888831    .0430853
                     90  |  -.0227161   .0338194    -0.67   0.502    -.0890009    .0435686
                     91  |  -.0225344   .0339671    -0.66   0.507    -.0891087    .0440399
                     92  |  -.0223537   .0341094    -0.66   0.512    -.0892068    .0444995
                     93  |   -.022174   .0342462    -0.65   0.517    -.0892954    .0449474
                     94  |  -.0219954   .0343778    -0.64   0.522    -.0893746    .0453839
                     95  |  -.0218178   .0345041    -0.63   0.527    -.0894446     .045809
                     96  |  -.0216413   .0346253    -0.63   0.532    -.0895056     .046223
                     97  |  -.0214659   .0347414    -0.62   0.537    -.0895578    .0466259
                     98  |  -.0212917   .0348525    -0.61   0.541    -.0896013     .047018
                     99  |  -.0211185   .0349587    -0.60   0.546    -.0896363    .0473994
                    100  |  -.0209464   .0350601    -0.60   0.550    -.0896629    .0477702
                    101  |  -.0207754   .0351567    -0.59   0.555    -.0896813    .0481305
                    102  |  -.0206055   .0352487    -0.58   0.559    -.0896917    .0484807
                    103  |  -.0204368   .0353361    -0.58   0.563    -.0896943    .0488207
                    104  |  -.0202692    .035419    -0.57   0.567    -.0896891    .0491507
                    105  |  -.0201027   .0354974    -0.57   0.571    -.0896764     .049471
                    106  |  -.0199374   .0355715    -0.56   0.575    -.0896563    .0497815
                    107  |  -.0197732   .0356413    -0.55   0.579    -.0896289    .0500826
                    108  |  -.0196101    .035707    -0.55   0.583    -.0895945    .0503742
                    109  |  -.0194482   .0357684    -0.54   0.587     -.089553    .0506567
                    110  |  -.0192874   .0358259    -0.54   0.590    -.0895048      .05093
                    111  |  -.0191277   .0358793    -0.53   0.594    -.0894498    .0511944
                    112  |  -.0189692   .0359288    -0.53   0.598    -.0893884    .0514499
                    113  |  -.0188118   .0359745    -0.52   0.601    -.0893205    .0516968
                    114  |  -.0186556   .0360164    -0.52   0.604    -.0892464    .0519352
                    115  |  -.0185005   .0360546    -0.51   0.608    -.0891662    .0521651
                    116  |  -.0183466   .0360891    -0.51   0.611    -.0890799    .0523868
                    117  |  -.0181937   .0361201    -0.50   0.614    -.0889878    .0526004
                    118  |   -.018042   .0361476    -0.50   0.618      -.08889    .0528059
                    119  |  -.0178915   .0361716    -0.49   0.621    -.0887865    .0530036
                    120  |   -.017742   .0361923    -0.49   0.624    -.0886776    .0531935
                    121  |  -.0175937   .0362096    -0.49   0.627    -.0885632    .0533758
                    122  |  -.0174465   .0362237    -0.48   0.630    -.0884437    .0535507
                    123  |  -.0173004   .0362346    -0.48   0.633    -.0883189    .0537181
                    124  |  -.0171555   .0362424    -0.47   0.636    -.0881892    .0538783
                    125  |  -.0170116   .0362471    -0.47   0.639    -.0880546    .0540314
                    126  |  -.0168688   .0362488    -0.47   0.642    -.0879151    .0541774
                    127  |  -.0167272   .0362475    -0.46   0.644     -.087771    .0543166
                    128  |  -.0165866   .0362434    -0.46   0.647    -.0876223     .054449
                    129  |  -.0164472   .0362364    -0.45   0.650    -.0874691    .0545748
                    130  |  -.0163088   .0362266    -0.45   0.653    -.0873116     .054694
                    131  |  -.0161715   .0362141    -0.45   0.655    -.0871497    .0548068
                    132  |  -.0160353   .0361989    -0.44   0.658    -.0869838    .0549132
                    133  |  -.0159001   .0361811    -0.44   0.660    -.0868137    .0550135
                    134  |   -.015766   .0361607    -0.44   0.663    -.0866397    .0551076
                    135  |   -.015633   .0361378    -0.43   0.665    -.0864618    .0551957
                    136  |  -.0155011   .0361124    -0.43   0.668    -.0862801     .055278
                    137  |  -.0153701   .0360846    -0.43   0.670    -.0860947    .0553544
                    138  |  -.0152403   .0360545    -0.42   0.673    -.0859057    .0554252
                    139  |  -.0151114    .036022    -0.42   0.675    -.0857132    .0554903
                    140  |  -.0149836   .0359872    -0.42   0.677    -.0855173      .05555
                    141  |  -.0148569   .0359502    -0.41   0.679     -.085318    .0556043
                    142  |  -.0147311    .035911    -0.41   0.682    -.0851155    .0556532
                    143  |  -.0146064   .0358697    -0.41   0.684    -.0849098     .055697
                    144  |  -.0144826   .0358263    -0.40   0.686     -.084701    .0557357
                    145  |  -.0143599   .0357809    -0.40   0.688    -.0844892    .0557693
                    146  |  -.0142382   .0357334    -0.40   0.690    -.0842744     .055798
                    147  |  -.0141175    .035684    -0.40   0.692    -.0840568    .0558219
                    148  |  -.0139977   .0356326    -0.39   0.694    -.0838364     .055841
                    149  |  -.0138789   .0355794    -0.39   0.696    -.0836133    .0558554
                    150  |  -.0137611   .0355243    -0.39   0.698    -.0833875    .0558653
                    151  |  -.0136443   .0354675    -0.38   0.700    -.0831592    .0558707
                    152  |  -.0135284   .0354088    -0.38   0.702    -.0829284    .0558716
                    153  |  -.0134134   .0353484    -0.38   0.704    -.0826951    .0558682
                    154  |  -.0132994   .0352864    -0.38   0.706    -.0824595    .0558606
                    155  |  -.0131864   .0352227    -0.37   0.708    -.0822216    .0558488
                    156  |  -.0130743   .0351574    -0.37   0.710    -.0819815    .0558329
                    157  |  -.0129631   .0350905    -0.37   0.712    -.0817391     .055813
                    158  |  -.0128528    .035022    -0.37   0.714    -.0814947    .0557891
                    159  |  -.0127434   .0349521    -0.36   0.715    -.0812483    .0557614
                    160  |   -.012635   .0348807    -0.36   0.717    -.0809998    .0557299
                    161  |  -.0125274   .0348078    -0.36   0.719    -.0807495    .0556946
                    162  |  -.0124208   .0347335    -0.36   0.721    -.0804972    .0556557
                    163  |   -.012315   .0346579    -0.36   0.722    -.0802432    .0556132
                    164  |  -.0122101   .0345809    -0.35   0.724    -.0799874    .0555672
                    165  |  -.0121061   .0345026    -0.35   0.726    -.0797299    .0555178
                    166  |  -.0120029    .034423    -0.35   0.727    -.0794708     .055465
                    167  |  -.0119006   .0343422    -0.35   0.729    -.0792101    .0554088
                    168  |  -.0117992   .0342601    -0.34   0.731    -.0789478    .0553495
                    169  |  -.0116986   .0341769    -0.34   0.732     -.078684    .0552869
                    170  |  -.0115988   .0340925    -0.34   0.734    -.0784189    .0552212
                    171  |  -.0114999   .0340069    -0.34   0.735    -.0781523    .0551525
                    172  |  -.0114018   .0339203    -0.34   0.737    -.0778844    .0550807
                    173  |  -.0113045   .0338326    -0.33   0.738    -.0776152    .0550061
                    174  |  -.0112081   .0337438    -0.33   0.740    -.0773447    .0549285
                    175  |  -.0111125    .033654    -0.33   0.741    -.0770731    .0548481
                    176  |  -.0110176   .0335632    -0.33   0.743    -.0768002     .054765
                    177  |  -.0109236   .0334714    -0.33   0.744    -.0765263    .0546792
                    178  |  -.0108303   .0333787    -0.32   0.746    -.0762513    .0545907
                    179  |  -.0107379    .033285    -0.32   0.747    -.0759753    .0544996
                    180  |  -.0106462   .0331905    -0.32   0.748    -.0756984     .054406
                    181  |  -.0105553   .0330951    -0.32   0.750    -.0754204    .0543099
                    182  |  -.0104651   .0329988    -0.32   0.751    -.0751416    .0542113
                    183  |  -.0103757   .0329017    -0.32   0.752    -.0748619    .0541104
                    184  |  -.0102871   .0328038    -0.31   0.754    -.0745814    .0540071
                    185  |  -.0101992   .0327051    -0.31   0.755    -.0743001    .0539016
                    186  |  -.0101121   .0326057    -0.31   0.756     -.074018    .0537938
                    187  |  -.0100257   .0325055    -0.31   0.758    -.0737352    .0536839
                    188  |    -.00994   .0324046    -0.31   0.759    -.0734518    .0535718
                    189  |  -.0098551    .032303    -0.31   0.760    -.0731677    .0534576
                    190  |  -.0097708   .0322007    -0.30   0.762     -.072883    .0533414
                    191  |  -.0096873   .0320978    -0.30   0.763    -.0725978    .0532231
                    192  |  -.0096045   .0319942    -0.30   0.764     -.072312     .053103
                    193  |  -.0095224     .03189    -0.30   0.765    -.0720257    .0529809
                    194  |   -.009441   .0317852    -0.30   0.766    -.0717389    .0528569
                    195  |  -.0093603   .0316799    -0.30   0.768    -.0714517    .0527312
                    196  |  -.0092802    .031574    -0.29   0.769    -.0711641    .0526036
                    197  |  -.0092009   .0314675    -0.29   0.770    -.0708761    .0524743
------------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. marginsplot, ///
>         recast(line) plot1opts(lcolor(gs8)) ciopt(color(black%20)) recastci(rarea) ///
>         ytitle("AME", size(medsmall)) ///
>         xtitle("Troops", size(medsmall))  ///
>         title("New democracy", size(medium)) yline(0, lcolor(red)) ///
>         addplot(hist troops, /// adding histogram marginsplot graph
> blcolor(black%30) fcolor(white%10) /// bar line and fill colors
> percent /// histogram bins in "percent" rather than "discrete" (actual)
> yaxis(2) /// calls for 2nd y-axis
> yscale(alt lcolor() axis(2)) /// scaling on 2nd y-axis
> ylabel(0 "0%" 20 "20%" 40 "40%" 60 "60%" 80 "80%" 100 "100%", /// labels on 2nd y-axis
> labcolor() axis(2) tlcolor(black) tlwidth(thin) labsize(small)) /// label options on 2nd y-axis
> ytitle(" ", axis(2)))  legend(off)

Variables that uniquely identify margins: troops

. graph save "figures\Study_2\B2.8.gph", replace
file figures\Study_2\B2.8.gph saved

. 
end of do-file

. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. 
. graph combine "figures\Study_2\B2.2.gph" "figures\Study_2\B2.4.gph" "figures\Study_2\B2.8.gph", col(2) 

. graph export "figures\Study_2\Figure_B3_AME_by_troop_rounded.gph", as(png) name("Graph") replace
(file figures\Study_2\Figure_B3_AME_by_troop_rounded.gph not found)
file figures\Study_2\Figure_B3_AME_by_troop_rounded.gph saved as PNG format

. 
end of do-file

. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. graph combine "figures\Study_2\Figure_2_original.gph" "figures\Study_2\Figure_2_AME_Regime_Type.gph" "figures\Study_2\Figure_B3_AME_by_troop_rounded.gph", col(2) 
file figures\Study_2\Figure_2_AME_Regime_Type.gph not found
r(601);

end of do-file

r(601);

. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. 
. *------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------*
. * Correctly estimated AME
. *------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------*
.         
. *model 2a: autocracy
. poisson milcoupsum robust_autocracy6##c.troops population2000 gdpcap durable conflict milexp exsol, cluster(ccode)

Iteration 0:   log pseudolikelihood = -449.22112  
Iteration 1:   log pseudolikelihood = -410.06911  
Iteration 2:   log pseudolikelihood = -404.01836  
Iteration 3:   log pseudolikelihood = -402.11343  
Iteration 4:   log pseudolikelihood = -402.01812  
Iteration 5:   log pseudolikelihood = -402.01764  
Iteration 6:   log pseudolikelihood = -402.01764  

Poisson regression                                      Number of obs =  3,001
                                                        Wald chi2(9)  =  82.80
Log pseudolikelihood = -402.01764                       Prob > chi2   = 0.0000

                                              (Std. err. adjusted for 148 clusters in ccode)
--------------------------------------------------------------------------------------------
                           |               Robust
                milcoupsum | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------------+----------------------------------------------------------------
       1.robust_autocracy6 |   .5278635   .3128468     1.69   0.092    -.0853049    1.141032
                    troops |  -1.838259   1.027145    -1.79   0.074    -3.851427    .1749095
                           |
robust_autocracy6#c.troops |
                        1  |   2.053367   1.009086     2.03   0.042     .0755952    4.031138
                           |
            population2000 |  -2.34e-09   6.43e-09    -0.36   0.716    -1.49e-08    1.03e-08
                    gdpcap |   -.020064   .0477717    -0.42   0.674    -.1136947    .0735667
                   durable |  -.0224258   .0221588    -1.01   0.312    -.0658563    .0210046
                  conflict |   .9358275   .3751772     2.49   0.013     .2004936    1.671161
                    milexp |  -378.6024   181.1369    -2.09   0.037    -733.6242   -23.58055
                     exsol |   .9254581   2.147148     0.43   0.666    -3.282875    5.133792
                     _cons |  -2.714514   .3781303    -7.18   0.000    -3.455636   -1.973392
--------------------------------------------------------------------------------------------

. margins, dydx(robust_autocracy6) post

Average marginal effects                                 Number of obs = 3,001
Model VCE: Robust

Expression: Predicted number of events, predict()
dy/dx wrt:  1.robust_autocracy6

-------------------------------------------------------------------------------------
                    |            Delta-method
                    |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
--------------------+----------------------------------------------------------------
1.robust_autocracy6 |   .0325869   .0163034     2.00   0.046     .0006328     .064541
-------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. est store m2a

. 
. 
. *model 4a: anocracy
. poisson milcoupsum robust_anocracy5##c.troops population2000 gdpcap durable conflict milexp exsol, cluster(ccode)

Iteration 0:   log pseudolikelihood = -418.40873  
Iteration 1:   log pseudolikelihood =  -412.0033  
Iteration 2:   log pseudolikelihood =   -407.598  
Iteration 3:   log pseudolikelihood = -405.16414  
Iteration 4:   log pseudolikelihood = -405.09412  
Iteration 5:   log pseudolikelihood = -405.09351  
Iteration 6:   log pseudolikelihood = -405.09351  

Poisson regression                                      Number of obs =  3,001
                                                        Wald chi2(9)  =  47.18
Log pseudolikelihood = -405.09351                       Prob > chi2   = 0.0000

                                             (Std. err. adjusted for 148 clusters in ccode)
-------------------------------------------------------------------------------------------
                          |               Robust
               milcoupsum | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------------------+----------------------------------------------------------------
       1.robust_anocracy5 |   .6782844   .2786318     2.43   0.015     .1321761    1.224393
                   troops |   .0150535   .2278477     0.07   0.947    -.4315197    .4616267
                          |
robust_anocracy5#c.troops |
                       1  |  -2.774543   .7840732    -3.54   0.000    -4.311299   -1.237788
                          |
           population2000 |  -4.52e-10   5.94e-09    -0.08   0.939    -1.21e-08    1.12e-08
                   gdpcap |  -.0128145   .0410951    -0.31   0.755    -.0933595    .0677304
                  durable |  -.0147644     .02168    -0.68   0.496    -.0572563    .0277276
                 conflict |   .9400657   .3531598     2.66   0.008     .2478853    1.632246
                   milexp |  -426.8629   172.5747    -2.47   0.013    -765.1032   -88.62272
                    exsol |   1.080705   2.444078     0.44   0.658    -3.709601     5.87101
                    _cons |  -3.068939   .3479924    -8.82   0.000    -3.750991   -2.386886
-------------------------------------------------------------------------------------------

. margins, dydx(robust_anocracy5) post

Average marginal effects                                 Number of obs = 3,001
Model VCE: Robust

Expression: Predicted number of events, predict()
dy/dx wrt:  1.robust_anocracy5

------------------------------------------------------------------------------------
                   |            Delta-method
                   |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------------+----------------------------------------------------------------
1.robust_anocracy5 |   .0183265   .0096998     1.89   0.059    -.0006847    .0373377
------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. est store m4a

. 
. 
. *model 8a: New democracy
. eststo: poisson milcoupsum new_robust_democracy6##c.troops population2000 gdpcap durable conflict milexp exsol, cluster(ccode)

Iteration 0:   log pseudolikelihood = -411.78171  
Iteration 1:   log pseudolikelihood = -406.87226  
Iteration 2:   log pseudolikelihood = -403.34415  
Iteration 3:   log pseudolikelihood = -400.72867  
Iteration 4:   log pseudolikelihood = -400.69906  
Iteration 5:   log pseudolikelihood = -400.69903  

Poisson regression                                      Number of obs =  3,001
                                                        Wald chi2(9)  =  72.73
Log pseudolikelihood = -400.69903                       Prob > chi2   = 0.0000

                                                  (Std. err. adjusted for 148 clusters in ccode)
------------------------------------------------------------------------------------------------
                               |               Robust
                    milcoupsum | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------------------------+----------------------------------------------------------------
       1.new_robust_democracy6 |  -1.118677   .4094854    -2.73   0.006    -1.921254   -.3161008
                        troops |  -.1720984   .3543506    -0.49   0.627    -.8666128    .5224159
                               |
new_robust_democracy6#c.troops |
                            1  |  -.6602458   .7434278    -0.89   0.374    -2.117338     .796846
                               |
                population2000 |  -3.33e-10   6.06e-09    -0.05   0.956    -1.22e-08    1.15e-08
                        gdpcap |   -.007618   .0313899    -0.24   0.808    -.0691412    .0539051
                       durable |  -.0167517    .021227    -0.79   0.430    -.0583558    .0248523
                      conflict |   .8082747   .3447763     2.34   0.019     .1325255    1.484024
                        milexp |  -398.4421   171.2426    -2.33   0.020    -734.0715   -62.81271
                         exsol |   .7746237   2.123611     0.36   0.715    -3.387578    4.936825
                         _cons |  -2.430278    .319332    -7.61   0.000    -3.056157   -1.804399
------------------------------------------------------------------------------------------------
(est12 stored)

. margins, dydx(new_robust_democracy6) post

Average marginal effects                                 Number of obs = 3,001
Model VCE: Robust

Expression: Predicted number of events, predict()
dy/dx wrt:  1.new_robust_democracy6

-----------------------------------------------------------------------------------------
                        |            Delta-method
                        |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
------------------------+----------------------------------------------------------------
1.new_robust_democracy6 |  -.0333935    .009051    -3.69   0.000    -.0511331   -.0156538
-----------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. est store m8a

. 
. 
. *coefplot
. coefplot (m2a, label(Autocracy) offset(-0.1)) ///
> (m4a, label(Anocracy) offset(-0.1)) ///
> (m8a, label(New Democracy) offset(-0.1)), xline(0) ///
> title("", size(medsmall) span) xtitle("Average Marginal Effects", size(small)) ylabel(,labsize(vsmall)) xlabel(,labsize(vsmall)) legend(size(vsmall) span)

. 
. graph export "figures\Study_2\Figure_2_AME_Regime_Type.gph", replace
output file suffix gph not recognized
    Specify correct suffix or specify as() option.
r(198);

end of do-file

r(198);

.  do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. graph combine "figures\Study_2\Figure_2_original.gph" "figures\Study_2\Figure_2_AME_Regime_Type.gph" "figures\Study_2\Figure_B3_AME_by_troop_rounded.gph", col(2) 
file figures\Study_2\Figure_2_AME_Regime_Type.gph not found
r(601);

end of do-file

r(601);

. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. graph combine "figures\Study_2\Figure_2_original.gph" "figures\Study_2\Figure_2_AME_Regime_Type.gph" "figures\Study_2\Figure_B3_AME_by_troop_rounded.gph", col(2) 
file figures\Study_2\Figure_2_AME_Regime_Type.gph not found
r(601);

end of do-file

r(601);

. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. *------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------*
. * Correctly estimated AME
. *------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------*
.         
. *model 2a: autocracy
. poisson milcoupsum robust_autocracy6##c.troops population2000 gdpcap durable conflict milexp exsol, cluster(ccode)

Iteration 0:   log pseudolikelihood = -449.22112  
Iteration 1:   log pseudolikelihood = -410.06911  
Iteration 2:   log pseudolikelihood = -404.01836  
Iteration 3:   log pseudolikelihood = -402.11343  
Iteration 4:   log pseudolikelihood = -402.01812  
Iteration 5:   log pseudolikelihood = -402.01764  
Iteration 6:   log pseudolikelihood = -402.01764  

Poisson regression                                      Number of obs =  3,001
                                                        Wald chi2(9)  =  82.80
Log pseudolikelihood = -402.01764                       Prob > chi2   = 0.0000

                                              (Std. err. adjusted for 148 clusters in ccode)
--------------------------------------------------------------------------------------------
                           |               Robust
                milcoupsum | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------------+----------------------------------------------------------------
       1.robust_autocracy6 |   .5278635   .3128468     1.69   0.092    -.0853049    1.141032
                    troops |  -1.838259   1.027145    -1.79   0.074    -3.851427    .1749095
                           |
robust_autocracy6#c.troops |
                        1  |   2.053367   1.009086     2.03   0.042     .0755952    4.031138
                           |
            population2000 |  -2.34e-09   6.43e-09    -0.36   0.716    -1.49e-08    1.03e-08
                    gdpcap |   -.020064   .0477717    -0.42   0.674    -.1136947    .0735667
                   durable |  -.0224258   .0221588    -1.01   0.312    -.0658563    .0210046
                  conflict |   .9358275   .3751772     2.49   0.013     .2004936    1.671161
                    milexp |  -378.6024   181.1369    -2.09   0.037    -733.6242   -23.58055
                     exsol |   .9254581   2.147148     0.43   0.666    -3.282875    5.133792
                     _cons |  -2.714514   .3781303    -7.18   0.000    -3.455636   -1.973392
--------------------------------------------------------------------------------------------

. margins, dydx(robust_autocracy6) post

Average marginal effects                                 Number of obs = 3,001
Model VCE: Robust

Expression: Predicted number of events, predict()
dy/dx wrt:  1.robust_autocracy6

-------------------------------------------------------------------------------------
                    |            Delta-method
                    |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
--------------------+----------------------------------------------------------------
1.robust_autocracy6 |   .0325869   .0163034     2.00   0.046     .0006328     .064541
-------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. est store m2a

. 
. 
. *model 4a: anocracy
. poisson milcoupsum robust_anocracy5##c.troops population2000 gdpcap durable conflict milexp exsol, cluster(ccode)

Iteration 0:   log pseudolikelihood = -418.40873  
Iteration 1:   log pseudolikelihood =  -412.0033  
Iteration 2:   log pseudolikelihood =   -407.598  
Iteration 3:   log pseudolikelihood = -405.16414  
Iteration 4:   log pseudolikelihood = -405.09412  
Iteration 5:   log pseudolikelihood = -405.09351  
Iteration 6:   log pseudolikelihood = -405.09351  

Poisson regression                                      Number of obs =  3,001
                                                        Wald chi2(9)  =  47.18
Log pseudolikelihood = -405.09351                       Prob > chi2   = 0.0000

                                             (Std. err. adjusted for 148 clusters in ccode)
-------------------------------------------------------------------------------------------
                          |               Robust
               milcoupsum | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------------------+----------------------------------------------------------------
       1.robust_anocracy5 |   .6782844   .2786318     2.43   0.015     .1321761    1.224393
                   troops |   .0150535   .2278477     0.07   0.947    -.4315197    .4616267
                          |
robust_anocracy5#c.troops |
                       1  |  -2.774543   .7840732    -3.54   0.000    -4.311299   -1.237788
                          |
           population2000 |  -4.52e-10   5.94e-09    -0.08   0.939    -1.21e-08    1.12e-08
                   gdpcap |  -.0128145   .0410951    -0.31   0.755    -.0933595    .0677304
                  durable |  -.0147644     .02168    -0.68   0.496    -.0572563    .0277276
                 conflict |   .9400657   .3531598     2.66   0.008     .2478853    1.632246
                   milexp |  -426.8629   172.5747    -2.47   0.013    -765.1032   -88.62272
                    exsol |   1.080705   2.444078     0.44   0.658    -3.709601     5.87101
                    _cons |  -3.068939   .3479924    -8.82   0.000    -3.750991   -2.386886
-------------------------------------------------------------------------------------------

. margins, dydx(robust_anocracy5) post

Average marginal effects                                 Number of obs = 3,001
Model VCE: Robust

Expression: Predicted number of events, predict()
dy/dx wrt:  1.robust_anocracy5

------------------------------------------------------------------------------------
                   |            Delta-method
                   |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------------+----------------------------------------------------------------
1.robust_anocracy5 |   .0183265   .0096998     1.89   0.059    -.0006847    .0373377
------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. est store m4a

. 
. 
. *model 8a: New democracy
. eststo: poisson milcoupsum new_robust_democracy6##c.troops population2000 gdpcap durable conflict milexp exsol, cluster(ccode)

Iteration 0:   log pseudolikelihood = -411.78171  
Iteration 1:   log pseudolikelihood = -406.87226  
Iteration 2:   log pseudolikelihood = -403.34415  
Iteration 3:   log pseudolikelihood = -400.72867  
Iteration 4:   log pseudolikelihood = -400.69906  
Iteration 5:   log pseudolikelihood = -400.69903  

Poisson regression                                      Number of obs =  3,001
                                                        Wald chi2(9)  =  72.73
Log pseudolikelihood = -400.69903                       Prob > chi2   = 0.0000

                                                  (Std. err. adjusted for 148 clusters in ccode)
------------------------------------------------------------------------------------------------
                               |               Robust
                    milcoupsum | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------------------------+----------------------------------------------------------------
       1.new_robust_democracy6 |  -1.118677   .4094854    -2.73   0.006    -1.921254   -.3161008
                        troops |  -.1720984   .3543506    -0.49   0.627    -.8666128    .5224159
                               |
new_robust_democracy6#c.troops |
                            1  |  -.6602458   .7434278    -0.89   0.374    -2.117338     .796846
                               |
                population2000 |  -3.33e-10   6.06e-09    -0.05   0.956    -1.22e-08    1.15e-08
                        gdpcap |   -.007618   .0313899    -0.24   0.808    -.0691412    .0539051
                       durable |  -.0167517    .021227    -0.79   0.430    -.0583558    .0248523
                      conflict |   .8082747   .3447763     2.34   0.019     .1325255    1.484024
                        milexp |  -398.4421   171.2426    -2.33   0.020    -734.0715   -62.81271
                         exsol |   .7746237   2.123611     0.36   0.715    -3.387578    4.936825
                         _cons |  -2.430278    .319332    -7.61   0.000    -3.056157   -1.804399
------------------------------------------------------------------------------------------------
(est13 stored)

. margins, dydx(new_robust_democracy6) post

Average marginal effects                                 Number of obs = 3,001
Model VCE: Robust

Expression: Predicted number of events, predict()
dy/dx wrt:  1.new_robust_democracy6

-----------------------------------------------------------------------------------------
                        |            Delta-method
                        |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
------------------------+----------------------------------------------------------------
1.new_robust_democracy6 |  -.0333935    .009051    -3.69   0.000    -.0511331   -.0156538
-----------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. est store m8a

. 
. 
. *coefplot
. coefplot (m2a, label(Autocracy) offset(-0.1)) ///
> (m4a, label(Anocracy) offset(-0.1)) ///
> (m8a, label(New Democracy) offset(-0.1)), xline(0) ///
> title("", size(medsmall) span) xtitle("Average Marginal Effects", size(small)) ylabel(,labsize(vsmall)) xlabel(,labsize(vsmall)) legend(size(vsmall) span)

. 
. graph export "figures\Study_2\Figure_2_AME_Regime_Type.gph", replace
output file suffix gph not recognized
    Specify correct suffix or specify as() option.
r(198);

end of do-file

r(198);

. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. graph save "figures\Study_2\Figure_2_AME_Regime_Type.gph", replace
(file figures\Study_2\Figure_2_AME_Regime_Type.gph not found)
file figures\Study_2\Figure_2_AME_Regime_Type.gph saved

. 
end of do-file

.  do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. 
. *------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------*
. * Correctly estimated AME by troop size 
. *------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------*
. 
.         
. *model 2a: autocracy
. poisson milcoupsum robust_autocracy6##c.troops population2000 gdpcap durable conflict milexp exsol, cluster(ccode)

Iteration 0:   log pseudolikelihood = -449.22112  
Iteration 1:   log pseudolikelihood = -410.06911  
Iteration 2:   log pseudolikelihood = -404.01836  
Iteration 3:   log pseudolikelihood = -402.11343  
Iteration 4:   log pseudolikelihood = -402.01812  
Iteration 5:   log pseudolikelihood = -402.01764  
Iteration 6:   log pseudolikelihood = -402.01764  

Poisson regression                                      Number of obs =  3,001
                                                        Wald chi2(9)  =  82.80
Log pseudolikelihood = -402.01764                       Prob > chi2   = 0.0000

                                              (Std. err. adjusted for 148 clusters in ccode)
--------------------------------------------------------------------------------------------
                           |               Robust
                milcoupsum | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------------+----------------------------------------------------------------
       1.robust_autocracy6 |   .5278635   .3128468     1.69   0.092    -.0853049    1.141032
                    troops |  -1.838259   1.027145    -1.79   0.074    -3.851427    .1749095
                           |
robust_autocracy6#c.troops |
                        1  |   2.053367   1.009086     2.03   0.042     .0755952    4.031138
                           |
            population2000 |  -2.34e-09   6.43e-09    -0.36   0.716    -1.49e-08    1.03e-08
                    gdpcap |   -.020064   .0477717    -0.42   0.674    -.1136947    .0735667
                   durable |  -.0224258   .0221588    -1.01   0.312    -.0658563    .0210046
                  conflict |   .9358275   .3751772     2.49   0.013     .2004936    1.671161
                    milexp |  -378.6024   181.1369    -2.09   0.037    -733.6242   -23.58055
                     exsol |   .9254581   2.147148     0.43   0.666    -3.282875    5.133792
                     _cons |  -2.714514   .3781303    -7.18   0.000    -3.455636   -1.973392
--------------------------------------------------------------------------------------------

. margins, dydx(robust_autocracy6) at (troops = (0(0.05)9.8)) post

Average marginal effects                                 Number of obs = 3,001
Model VCE: Robust

Expression: Predicted number of events, predict()
dy/dx wrt:  1.robust_autocracy6
1._at:   troops =    0
2._at:   troops =  .05
3._at:   troops =   .1
4._at:   troops =  .15
5._at:   troops =   .2
6._at:   troops =  .25
7._at:   troops =   .3
8._at:   troops =  .35
9._at:   troops =   .4
10._at:  troops =  .45
11._at:  troops =   .5
12._at:  troops =  .55
13._at:  troops =   .6
14._at:  troops =  .65
15._at:  troops =   .7
16._at:  troops =  .75
17._at:  troops =   .8
18._at:  troops =  .85
19._at:  troops =   .9
20._at:  troops =  .95
21._at:  troops =    1
22._at:  troops = 1.05
23._at:  troops =  1.1
24._at:  troops = 1.15
25._at:  troops =  1.2
26._at:  troops = 1.25
27._at:  troops =  1.3
28._at:  troops = 1.35
29._at:  troops =  1.4
30._at:  troops = 1.45
31._at:  troops =  1.5
32._at:  troops = 1.55
33._at:  troops =  1.6
34._at:  troops = 1.65
35._at:  troops =  1.7
36._at:  troops = 1.75
37._at:  troops =  1.8
38._at:  troops = 1.85
39._at:  troops =  1.9
40._at:  troops = 1.95
41._at:  troops =    2
42._at:  troops = 2.05
43._at:  troops =  2.1
44._at:  troops = 2.15
45._at:  troops =  2.2
46._at:  troops = 2.25
47._at:  troops =  2.3
48._at:  troops = 2.35
49._at:  troops =  2.4
50._at:  troops = 2.45
51._at:  troops =  2.5
52._at:  troops = 2.55
53._at:  troops =  2.6
54._at:  troops = 2.65
55._at:  troops =  2.7
56._at:  troops = 2.75
57._at:  troops =  2.8
58._at:  troops = 2.85
59._at:  troops =  2.9
60._at:  troops = 2.95
61._at:  troops =    3
62._at:  troops = 3.05
63._at:  troops =  3.1
64._at:  troops = 3.15
65._at:  troops =  3.2
66._at:  troops = 3.25
67._at:  troops =  3.3
68._at:  troops = 3.35
69._at:  troops =  3.4
70._at:  troops = 3.45
71._at:  troops =  3.5
72._at:  troops = 3.55
73._at:  troops =  3.6
74._at:  troops = 3.65
75._at:  troops =  3.7
76._at:  troops = 3.75
77._at:  troops =  3.8
78._at:  troops = 3.85
79._at:  troops =  3.9
80._at:  troops = 3.95
81._at:  troops =    4
82._at:  troops = 4.05
83._at:  troops =  4.1
84._at:  troops = 4.15
85._at:  troops =  4.2
86._at:  troops = 4.25
87._at:  troops =  4.3
88._at:  troops = 4.35
89._at:  troops =  4.4
90._at:  troops = 4.45
91._at:  troops =  4.5
92._at:  troops = 4.55
93._at:  troops =  4.6
94._at:  troops = 4.65
95._at:  troops =  4.7
96._at:  troops = 4.75
97._at:  troops =  4.8
98._at:  troops = 4.85
99._at:  troops =  4.9
100._at: troops = 4.95
101._at: troops =    5
102._at: troops = 5.05
103._at: troops =  5.1
104._at: troops = 5.15
105._at: troops =  5.2
106._at: troops = 5.25
107._at: troops =  5.3
108._at: troops = 5.35
109._at: troops =  5.4
110._at: troops = 5.45
111._at: troops =  5.5
112._at: troops = 5.55
113._at: troops =  5.6
114._at: troops = 5.65
115._at: troops =  5.7
116._at: troops = 5.75
117._at: troops =  5.8
118._at: troops = 5.85
119._at: troops =  5.9
120._at: troops = 5.95
121._at: troops =    6
122._at: troops = 6.05
123._at: troops =  6.1
124._at: troops = 6.15
125._at: troops =  6.2
126._at: troops = 6.25
127._at: troops =  6.3
128._at: troops = 6.35
129._at: troops =  6.4
130._at: troops = 6.45
131._at: troops =  6.5
132._at: troops = 6.55
133._at: troops =  6.6
134._at: troops = 6.65
135._at: troops =  6.7
136._at: troops = 6.75
137._at: troops =  6.8
138._at: troops = 6.85
139._at: troops =  6.9
140._at: troops = 6.95
141._at: troops =    7
142._at: troops = 7.05
143._at: troops =  7.1
144._at: troops = 7.15
145._at: troops =  7.2
146._at: troops = 7.25
147._at: troops =  7.3
148._at: troops = 7.35
149._at: troops =  7.4
150._at: troops = 7.45
151._at: troops =  7.5
152._at: troops = 7.55
153._at: troops =  7.6
154._at: troops = 7.65
155._at: troops =  7.7
156._at: troops = 7.75
157._at: troops =  7.8
158._at: troops = 7.85
159._at: troops =  7.9
160._at: troops = 7.95
161._at: troops =    8
162._at: troops = 8.05
163._at: troops =  8.1
164._at: troops = 8.15
165._at: troops =  8.2
166._at: troops = 8.25
167._at: troops =  8.3
168._at: troops = 8.35
169._at: troops =  8.4
170._at: troops = 8.45
171._at: troops =  8.5
172._at: troops = 8.55
173._at: troops =  8.6
174._at: troops = 8.65
175._at: troops =  8.7
176._at: troops = 8.75
177._at: troops =  8.8
178._at: troops = 8.85
179._at: troops =  8.9
180._at: troops = 8.95
181._at: troops =    9
182._at: troops = 9.05
183._at: troops =  9.1
184._at: troops = 9.15
185._at: troops =  9.2
186._at: troops = 9.25
187._at: troops =  9.3
188._at: troops = 9.35
189._at: troops =  9.4
190._at: troops = 9.45
191._at: troops =  9.5
192._at: troops = 9.55
193._at: troops =  9.6
194._at: troops = 9.65
195._at: troops =  9.7
196._at: troops = 9.75
197._at: troops =  9.8

--------------------------------------------------------------------------------------
                     |            Delta-method
                     |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
---------------------+----------------------------------------------------------------
0.robust_autocracy6  |  (base outcome)
---------------------+----------------------------------------------------------------
1.robust_autocracy6  |
                 _at |
                  1  |   .0240775   .0166719     1.44   0.149    -.0085989    .0567539
                  2  |   .0277532   .0164575     1.69   0.092    -.0045028    .0600093
                  3  |   .0311688   .0164246     1.90   0.058    -.0010229    .0633605
                  4  |   .0343477   .0164992     2.08   0.037       .00201    .0666855
                  5  |   .0373115    .016629     2.24   0.025     .0047192    .0699037
                  6  |   .0400796   .0167799     2.39   0.017     .0071917    .0729675
                  7  |     .04267   .0169309     2.52   0.012      .009486     .075854
                  8  |    .045099   .0170705     2.64   0.008     .0116414    .0785566
                  9  |   .0473815   .0171934     2.76   0.006     .0136831    .0810799
                 10  |    .049531   .0172982     2.86   0.004     .0156271     .083435
                 11  |   .0515601   .0173863     2.97   0.003     .0174835    .0856366
                 12  |   .0534799   .0174603     3.06   0.002     .0192584    .0877014
                 13  |   .0553008   .0175235     3.16   0.002     .0209553    .0896464
                 14  |   .0570324   .0175799     3.24   0.001     .0225765    .0914883
                 15  |   .0586831   .0176329     3.33   0.001     .0241232     .093243
                 16  |   .0602608   .0176862     3.41   0.001     .0255965    .0949252
                 17  |   .0617728   .0177429     3.48   0.000     .0269973    .0965483
                 18  |   .0632256   .0178059     3.55   0.000     .0283266    .0981245
                 19  |   .0646252   .0178776     3.61   0.000     .0295857    .0996646
                 20  |    .065977     .01796     3.67   0.000      .030776     .101178
                 21  |   .0672861   .0180549     3.73   0.000     .0318992    .1026731
                 22  |   .0685571   .0181636     3.77   0.000     .0329571    .1041571
                 23  |   .0697941   .0182872     3.82   0.000     .0339518    .1056364
                 24  |   .0710009   .0184265     3.85   0.000     .0348856    .1071162
                 25  |   .0721811   .0185821     3.88   0.000     .0357608    .1086014
                 26  |   .0733378   .0187544     3.91   0.000     .0365798    .1100958
                 27  |    .074474   .0189436     3.93   0.000     .0373452    .1116029
                 28  |   .0755924   .0191499     3.95   0.000     .0380592    .1131256
                 29  |   .0766953   .0193733     3.96   0.000     .0387244    .1146663
                 30  |   .0777852   .0196137     3.97   0.000      .039343    .1162273
                 31  |   .0788639    .019871     3.97   0.000     .0399173    .1178104
                 32  |   .0799334   .0201452     3.97   0.000     .0404495    .1194173
                 33  |   .0809955   .0204361     3.96   0.000     .0409415    .1210494
                 34  |   .0820516   .0207434     3.96   0.000     .0413953     .122708
                 35  |   .0831034   .0210671     3.94   0.000     .0418126    .1243942
                 36  |   .0841521   .0214071     3.93   0.000      .042195    .1261091
                 37  |   .0851989   .0217631     3.91   0.000     .0425441    .1278538
                 38  |   .0862451   .0221351     3.90   0.000     .0428611     .129629
                 39  |   .0872916    .022523     3.88   0.000     .0431474    .1314358
                 40  |   .0883394   .0229266     3.85   0.000      .043404    .1332748
                 41  |   .0893894   .0233461     3.83   0.000      .043632    .1351469
                 42  |   .0904425   .0237812     3.80   0.000     .0438322    .1370528
                 43  |   .0914993   .0242321     3.78   0.000     .0440053    .1389933
                 44  |   .0925607   .0246986     3.75   0.000     .0441522    .1409691
                 45  |   .0936272   .0251809     3.72   0.000     .0442734    .1429809
                 46  |   .0946995    .025679     3.69   0.000     .0443695    .1450295
                 47  |   .0957781    .026193     3.66   0.000     .0444408    .1471154
                 48  |   .0968636   .0267228     3.62   0.000     .0444879    .1492393
                 49  |   .0979565   .0272687     3.59   0.000     .0445109     .151402
                 50  |   .0990571   .0278306     3.56   0.000     .0445102    .1536041
                 51  |   .1001661   .0284088     3.53   0.000      .044486    .1558462
                 52  |   .1012837   .0290032     3.49   0.000     .0444384     .158129
                 53  |   .1024104   .0296142     3.46   0.001     .0443677    .1604531
                 54  |   .1035465   .0302417     3.42   0.001     .0442738    .1628192
                 55  |   .1046924    .030886     3.39   0.001     .0441569    .1652278
                 56  |   .1058483   .0315472     3.36   0.001     .0440169    .1676797
                 57  |   .1070147   .0322255     3.32   0.001     .0438539    .1701754
                 58  |   .1081917   .0329209     3.29   0.001     .0436679    .1727156
                 59  |   .1093798   .0336338     3.25   0.001     .0434587    .1753008
                 60  |   .1105791   .0343643     3.22   0.001     .0432264    .1779318
                 61  |   .1117899   .0351125     3.18   0.001     .0429707    .1806091
                 62  |   .1130125   .0358786     3.15   0.002     .0426917    .1833333
                 63  |   .1142471   .0366629     3.12   0.002     .0423891     .186105
                 64  |   .1154939   .0374655     3.08   0.002     .0420629     .188925
                 65  |   .1167533   .0382867     3.05   0.002     .0417128    .1917938
                 66  |   .1180253   .0391266     3.02   0.003     .0413386    .1947119
                 67  |   .1193102   .0399854     2.98   0.003     .0409403    .1976802
                 68  |   .1206083   .0408634     2.95   0.003     .0405175    .2006991
                 69  |   .1219197   .0417608     2.92   0.004     .0400701    .2037693
                 70  |   .1232447   .0426777     2.89   0.004     .0395979    .2068915
                 71  |   .1245834   .0436145     2.86   0.004     .0391005    .2100663
                 72  |   .1259361   .0445714     2.83   0.005     .0385778    .2132944
                 73  |   .1273029   .0455485     2.79   0.005     .0380294    .2165763
                 74  |    .128684   .0465462     2.76   0.006     .0374551    .2199129
                 75  |   .1300797   .0475647     2.73   0.006     .0368547    .2233047
                 76  |   .1314901   .0486041     2.71   0.007     .0362278    .2267524
                 77  |   .1329154   .0496649     2.68   0.007     .0355741    .2302568
                 78  |   .1343559   .0507471     2.65   0.008     .0348933    .2338184
                 79  |   .1358116   .0518512     2.62   0.009     .0341851    .2374381
                 80  |   .1372828   .0529773     2.59   0.010     .0334492    .2411165
                 81  |   .1387697   .0541258     2.56   0.010     .0326852    .2448543
                 82  |   .1402725   .0552968     2.54   0.011     .0318927    .2486523
                 83  |   .1417913   .0564908     2.51   0.012     .0310714    .2525112
                 84  |   .1433264   .0577079     2.48   0.013      .030221    .2564318
                 85  |   .1448779   .0589485     2.46   0.014      .029341    .2604147
                 86  |    .146446   .0602128     2.43   0.015     .0284311    .2644609
                 87  |    .148031   .0615012     2.41   0.016     .0274909    .2685711
                 88  |    .149633   .0628139     2.38   0.017     .0265199     .272746
                 89  |   .1512522   .0641514     2.36   0.018     .0255178    .2769866
                 90  |   .1528888   .0655138     2.33   0.020     .0244841    .2812935
                 91  |    .154543   .0669015     2.31   0.021     .0234184    .2856676
                 92  |    .156215   .0683149     2.29   0.022     .0223203    .2901098
                 93  |    .157905   .0697543     2.26   0.024     .0211892    .2946209
                 94  |   .1596133   .0712199     2.24   0.025     .0200248    .2992018
                 95  |   .1613399   .0727123     2.22   0.026     .0188265    .3038533
                 96  |   .1630851   .0742316     2.20   0.028     .0175939    .3085764
                 97  |   .1648492   .0757783     2.18   0.030     .0163265    .3133719
                 98  |   .1666323   .0773527     2.15   0.031     .0150238    .3182408
                 99  |   .1684346   .0789552     2.13   0.033     .0136852     .323184
                100  |   .1702564   .0805862     2.11   0.035     .0123104    .3282024
                101  |   .1720978    .082246     2.09   0.036     .0108986    .3332971
                102  |   .1739592   .0839351     2.07   0.038     .0094494    .3384689
                103  |   .1758406   .0856537     2.05   0.040     .0079623    .3437188
                104  |   .1777423   .0874024     2.03   0.042     .0064367    .3490479
                105  |   .1796646   .0891815     2.01   0.044      .004872    .3544572
                106  |   .1816076   .0909915     2.00   0.046     .0032676    .3599477
                107  |   .1835716   .0928327     1.98   0.048     .0016229    .3655204
                108  |   .1855569   .0947055     1.96   0.050    -.0000626    .3711763
                109  |   .1875636   .0966105     1.94   0.052    -.0017896    .3769167
                110  |   .1895919    .098548     1.92   0.054    -.0035586    .3827425
                111  |   .1916422   .1005185     1.91   0.057    -.0053704    .3886549
                112  |   .1937147   .1025224     1.89   0.059    -.0072256     .394655
                113  |   .1958095   .1045602     1.87   0.061    -.0091247    .4007438
                114  |    .197927   .1066324     1.86   0.063    -.0110686    .4069226
                115  |   .2000674   .1087393     1.84   0.066    -.0130578    .4131926
                116  |   .2022309   .1108816     1.82   0.068     -.015093    .4195549
                117  |   .2044178   .1130596     1.81   0.071     -.017175    .4260107
                118  |   .2066284   .1152739     1.79   0.073    -.0193044    .4325611
                119  |   .2088628    .117525     1.78   0.076     -.021482    .4392076
                120  |   .2111214   .1198134     1.76   0.078    -.0237085    .4459513
                121  |   .2134044   .1221395     1.75   0.081    -.0259847    .4527935
                122  |   .2157121    .124504     1.73   0.083    -.0283112    .4597354
                123  |   .2180447   .1269073     1.72   0.086    -.0306889    .4667784
                124  |   .2204026   .1293499     1.70   0.088    -.0331186    .4739238
                125  |   .2227859   .1318325     1.69   0.091     -.035601    .4811729
                126  |   .2251951   .1343555     1.68   0.094    -.0381369    .4885271
                127  |   .2276303   .1369196     1.66   0.096    -.0407273    .4959878
                128  |   .2300917   .1395253     1.65   0.099    -.0433728    .5035562
                129  |   .2325799   .1421731     1.64   0.102    -.0460743     .511234
                130  |   .2350949   .1448637     1.62   0.105    -.0488327    .5190224
                131  |   .2376371   .1475976     1.61   0.107    -.0516489    .5269231
                132  |   .2402068   .1503755     1.60   0.110    -.0545237    .5349373
                133  |   .2428043   .1531979     1.58   0.113    -.0574581    .5430666
                134  |   .2454298   .1560655     1.57   0.116    -.0604529    .5513125
                135  |   .2480838   .1589788     1.56   0.119     -.063509    .5596766
                136  |   .2507665   .1619386     1.55   0.121    -.0666275    .5681604
                137  |   .2534781   .1649455     1.54   0.124    -.0698092    .5767654
                138  |   .2562191   .1680001     1.53   0.127    -.0730551    .5854933
                139  |   .2589897   .1711031     1.51   0.130    -.0763662    .5943457
                140  |   .2617903   .1742551     1.50   0.133    -.0797435    .6033241
                141  |   .2646212   .1774569     1.49   0.136     -.083188    .6124303
                142  |   .2674827   .1807091     1.48   0.139    -.0867007     .621666
                143  |   .2703751   .1840124     1.47   0.142    -.0902827    .6310328
                144  |   .2732988   .1873676     1.46   0.145     -.093935    .6405325
                145  |   .2762541   .1907753     1.45   0.148    -.0976586    .6501667
                146  |   .2792413   .1942362     1.44   0.151    -.1014547    .6599374
                147  |   .2822609   .1977512     1.43   0.153    -.1053244    .6698462
                148  |   .2853131    .201321     1.42   0.156    -.1092688     .679895
                149  |   .2883983   .2049463     1.41   0.159    -.1132889    .6900856
                150  |   .2915169   .2086278     1.40   0.162     -.117386    .7004198
                151  |   .2946692   .2123664     1.39   0.165    -.1215613    .7108997
                152  |   .2978556   .2161629     1.38   0.168    -.1258159    .7215271
                153  |   .3010765    .220018     1.37   0.171    -.1301509    .7323038
                154  |   .3043321   .2239326     1.36   0.174    -.1345677     .743232
                155  |    .307623   .2279075     1.35   0.177    -.1390674    .7543135
                156  |   .3109495   .2319435     1.34   0.180    -.1436514    .7655504
                157  |   .3143119   .2360415     1.33   0.183    -.1483209    .7769447
                158  |   .3177107   .2402022     1.32   0.186     -.153077    .7884985
                159  |   .3211463   .2444267     1.31   0.189    -.1579213    .8002139
                160  |    .324619   .2487158     1.31   0.192     -.162855    .8120929
                161  |   .3281292   .2530703     1.30   0.195    -.1678794    .8241378
                162  |   .3316774   .2574912     1.29   0.198    -.1729959    .8363508
                163  |    .335264   .2619793     1.28   0.201     -.178206     .848734
                164  |   .3388893   .2665356     1.27   0.204    -.1835108    .8612895
                165  |   .3425539   .2711611     1.26   0.206     -.188912    .8740198
                166  |   .3462581   .2758567     1.26   0.209     -.194411    .8869272
                167  |   .3500024   .2806233     1.25   0.212    -.2000092     .900014
                168  |   .3537871    .285462     1.24   0.215    -.2057081    .9132824
                169  |   .3576127   .2903736     1.23   0.218    -.2115091    .9267345
                170  |   .3614798   .2953594     1.22   0.221    -.2174139    .9403735
                171  |   .3653886   .3004201     1.22   0.224     -.223424    .9542013
                172  |   .3693397    .305557     1.21   0.227     -.229541    .9682205
                173  |   .3733336    .310771     1.20   0.230    -.2357664    .9824336
                174  |   .3773706   .3160631     1.19   0.232    -.2421017    .9968428
                175  |   .3814513   .3214345     1.19   0.235    -.2485488    1.011451
                176  |   .3855761   .3268863     1.18   0.238    -.2551092    1.026261
                177  |   .3897455   .3324195     1.17   0.241    -.2617847    1.041276
                178  |     .39396   .3380352     1.17   0.244    -.2685769    1.056497
                179  |     .39822   .3437346     1.16   0.247    -.2754874    1.071927
                180  |   .4025261   .3495189     1.15   0.249    -.2825182    1.087571
                181  |   .4068789   .3553891     1.14   0.252     -.289671    1.103429
                182  |   .4112786   .3613466     1.14   0.255    -.2969476    1.119505
                183  |    .415726   .3673924     1.13   0.258    -.3043498    1.135802
                184  |   .4202214   .3735276     1.13   0.261    -.3118794    1.152322
                185  |   .4247654   .3797538     1.12   0.263    -.3195384    1.169069
                186  |   .4293586    .386072     1.11   0.266    -.3273286    1.186046
                187  |   .4340015   .3924835     1.11   0.269     -.335252    1.203255
                188  |   .4386945   .3989895     1.10   0.272    -.3433105      1.2207
                189  |   .4434383   .4055913     1.09   0.274     -.351506    1.238383
                190  |   .4482334   .4122903     1.09   0.277    -.3598408    1.256308
                191  |   .4530803   .4190878     1.08   0.280    -.3683167    1.274477
                192  |   .4579797   .4259851     1.08   0.282    -.3769358    1.292895
                193  |   .4629321   .4329836     1.07   0.285    -.3857002    1.311564
                194  |   .4679379   .4400845     1.06   0.288    -.3946119    1.330488
                195  |   .4729979   .4472895     1.06   0.290    -.4036734    1.349669
                196  |   .4781127   .4545999     1.05   0.293    -.4128867    1.369112
                197  |   .4832828    .462017     1.05   0.296    -.4222539    1.388819
--------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. marginsplot, ///
>         recast(line) plot1opts(lcolor(gs8)) ciopt(color(black%20)) recastci(rarea) ///
>         ytitle("AME", size(medsmall)) ///
>         xtitle("Troops", size(medsmall))  ///
>         title("Autocracy", size(medium)) yline(0, lcolor(red)) ///
>         addplot(hist troops, /// adding histogram marginsplot graph
> blcolor(black%30) fcolor(white%10) /// bar line and fill colors
> percent /// histogram bins in "percent" rather than "discrete" (actual)
> yaxis(2) /// calls for 2nd y-axis
> yscale(alt lcolor() axis(2)) /// scaling on 2nd y-axis
> ylabel(0 "0%" 20 "20%" 40 "40%" 60 "60%" 80 "80%" 100 "100%", /// labels on 2nd y-axis
> labcolor() axis(2) tlcolor(black) tlwidth(thin) labsize(small)) /// label options on 2nd y-axis
> ytitle(" ", axis(2)))  legend(off)

Variables that uniquely identify margins: troops

. graph save "figures\Study_2\B2.2.gph", replace
file figures\Study_2\B2.2.gph saved

. 
. 
. *model 4a: anocracy
. poisson milcoupsum robust_anocracy5##c.troops population2000 gdpcap durable conflict milexp exsol, cluster(ccode)

Iteration 0:   log pseudolikelihood = -418.40873  
Iteration 1:   log pseudolikelihood =  -412.0033  
Iteration 2:   log pseudolikelihood =   -407.598  
Iteration 3:   log pseudolikelihood = -405.16414  
Iteration 4:   log pseudolikelihood = -405.09412  
Iteration 5:   log pseudolikelihood = -405.09351  
Iteration 6:   log pseudolikelihood = -405.09351  

Poisson regression                                      Number of obs =  3,001
                                                        Wald chi2(9)  =  47.18
Log pseudolikelihood = -405.09351                       Prob > chi2   = 0.0000

                                             (Std. err. adjusted for 148 clusters in ccode)
-------------------------------------------------------------------------------------------
                          |               Robust
               milcoupsum | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------------------+----------------------------------------------------------------
       1.robust_anocracy5 |   .6782844   .2786318     2.43   0.015     .1321761    1.224393
                   troops |   .0150535   .2278477     0.07   0.947    -.4315197    .4616267
                          |
robust_anocracy5#c.troops |
                       1  |  -2.774543   .7840732    -3.54   0.000    -4.311299   -1.237788
                          |
           population2000 |  -4.52e-10   5.94e-09    -0.08   0.939    -1.21e-08    1.12e-08
                   gdpcap |  -.0128145   .0410951    -0.31   0.755    -.0933595    .0677304
                  durable |  -.0147644     .02168    -0.68   0.496    -.0572563    .0277276
                 conflict |   .9400657   .3531598     2.66   0.008     .2478853    1.632246
                   milexp |  -426.8629   172.5747    -2.47   0.013    -765.1032   -88.62272
                    exsol |   1.080705   2.444078     0.44   0.658    -3.709601     5.87101
                    _cons |  -3.068939   .3479924    -8.82   0.000    -3.750991   -2.386886
-------------------------------------------------------------------------------------------

. margins, dydx(robust_anocracy5) at (troops =  (0(0.05)9.8)) post

Average marginal effects                                 Number of obs = 3,001
Model VCE: Robust

Expression: Predicted number of events, predict()
dy/dx wrt:  1.robust_anocracy5
1._at:   troops =    0
2._at:   troops =  .05
3._at:   troops =   .1
4._at:   troops =  .15
5._at:   troops =   .2
6._at:   troops =  .25
7._at:   troops =   .3
8._at:   troops =  .35
9._at:   troops =   .4
10._at:  troops =  .45
11._at:  troops =   .5
12._at:  troops =  .55
13._at:  troops =   .6
14._at:  troops =  .65
15._at:  troops =   .7
16._at:  troops =  .75
17._at:  troops =   .8
18._at:  troops =  .85
19._at:  troops =   .9
20._at:  troops =  .95
21._at:  troops =    1
22._at:  troops = 1.05
23._at:  troops =  1.1
24._at:  troops = 1.15
25._at:  troops =  1.2
26._at:  troops = 1.25
27._at:  troops =  1.3
28._at:  troops = 1.35
29._at:  troops =  1.4
30._at:  troops = 1.45
31._at:  troops =  1.5
32._at:  troops = 1.55
33._at:  troops =  1.6
34._at:  troops = 1.65
35._at:  troops =  1.7
36._at:  troops = 1.75
37._at:  troops =  1.8
38._at:  troops = 1.85
39._at:  troops =  1.9
40._at:  troops = 1.95
41._at:  troops =    2
42._at:  troops = 2.05
43._at:  troops =  2.1
44._at:  troops = 2.15
45._at:  troops =  2.2
46._at:  troops = 2.25
47._at:  troops =  2.3
48._at:  troops = 2.35
49._at:  troops =  2.4
50._at:  troops = 2.45
51._at:  troops =  2.5
52._at:  troops = 2.55
53._at:  troops =  2.6
54._at:  troops = 2.65
55._at:  troops =  2.7
56._at:  troops = 2.75
57._at:  troops =  2.8
58._at:  troops = 2.85
59._at:  troops =  2.9
60._at:  troops = 2.95
61._at:  troops =    3
62._at:  troops = 3.05
63._at:  troops =  3.1
64._at:  troops = 3.15
65._at:  troops =  3.2
66._at:  troops = 3.25
67._at:  troops =  3.3
68._at:  troops = 3.35
69._at:  troops =  3.4
70._at:  troops = 3.45
71._at:  troops =  3.5
72._at:  troops = 3.55
73._at:  troops =  3.6
74._at:  troops = 3.65
75._at:  troops =  3.7
76._at:  troops = 3.75
77._at:  troops =  3.8
78._at:  troops = 3.85
79._at:  troops =  3.9
80._at:  troops = 3.95
81._at:  troops =    4
82._at:  troops = 4.05
83._at:  troops =  4.1
84._at:  troops = 4.15
85._at:  troops =  4.2
86._at:  troops = 4.25
87._at:  troops =  4.3
88._at:  troops = 4.35
89._at:  troops =  4.4
90._at:  troops = 4.45
91._at:  troops =  4.5
92._at:  troops = 4.55
93._at:  troops =  4.6
94._at:  troops = 4.65
95._at:  troops =  4.7
96._at:  troops = 4.75
97._at:  troops =  4.8
98._at:  troops = 4.85
99._at:  troops =  4.9
100._at: troops = 4.95
101._at: troops =    5
102._at: troops = 5.05
103._at: troops =  5.1
104._at: troops = 5.15
105._at: troops =  5.2
106._at: troops = 5.25
107._at: troops =  5.3
108._at: troops = 5.35
109._at: troops =  5.4
110._at: troops = 5.45
111._at: troops =  5.5
112._at: troops = 5.55
113._at: troops =  5.6
114._at: troops = 5.65
115._at: troops =  5.7
116._at: troops = 5.75
117._at: troops =  5.8
118._at: troops = 5.85
119._at: troops =  5.9
120._at: troops = 5.95
121._at: troops =    6
122._at: troops = 6.05
123._at: troops =  6.1
124._at: troops = 6.15
125._at: troops =  6.2
126._at: troops = 6.25
127._at: troops =  6.3
128._at: troops = 6.35
129._at: troops =  6.4
130._at: troops = 6.45
131._at: troops =  6.5
132._at: troops = 6.55
133._at: troops =  6.6
134._at: troops = 6.65
135._at: troops =  6.7
136._at: troops = 6.75
137._at: troops =  6.8
138._at: troops = 6.85
139._at: troops =  6.9
140._at: troops = 6.95
141._at: troops =    7
142._at: troops = 7.05
143._at: troops =  7.1
144._at: troops = 7.15
145._at: troops =  7.2
146._at: troops = 7.25
147._at: troops =  7.3
148._at: troops = 7.35
149._at: troops =  7.4
150._at: troops = 7.45
151._at: troops =  7.5
152._at: troops = 7.55
153._at: troops =  7.6
154._at: troops = 7.65
155._at: troops =  7.7
156._at: troops = 7.75
157._at: troops =  7.8
158._at: troops = 7.85
159._at: troops =  7.9
160._at: troops = 7.95
161._at: troops =    8
162._at: troops = 8.05
163._at: troops =  8.1
164._at: troops = 8.15
165._at: troops =  8.2
166._at: troops = 8.25
167._at: troops =  8.3
168._at: troops = 8.35
169._at: troops =  8.4
170._at: troops = 8.45
171._at: troops =  8.5
172._at: troops = 8.55
173._at: troops =  8.6
174._at: troops = 8.65
175._at: troops =  8.7
176._at: troops = 8.75
177._at: troops =  8.8
178._at: troops = 8.85
179._at: troops =  8.9
180._at: troops = 8.95
181._at: troops =    9
182._at: troops = 9.05
183._at: troops =  9.1
184._at: troops = 9.15
185._at: troops =  9.2
186._at: troops = 9.25
187._at: troops =  9.3
188._at: troops = 9.35
189._at: troops =  9.4
190._at: troops = 9.45
191._at: troops =  9.5
192._at: troops = 9.55
193._at: troops =  9.6
194._at: troops = 9.65
195._at: troops =  9.7
196._at: troops = 9.75
197._at: troops =  9.8

-------------------------------------------------------------------------------------
                    |            Delta-method
                    |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
--------------------+----------------------------------------------------------------
0.robust_anocracy5  |  (base outcome)
--------------------+----------------------------------------------------------------
1.robust_anocracy5  |
                _at |
                 1  |   .0262459   .0113033     2.32   0.020     .0040918    .0483999
                 2  |   .0193576   .0099818     1.94   0.052    -.0002065    .0389216
                 3  |   .0133544   .0090922     1.47   0.142     -.004466    .0311748
                 4  |   .0081223   .0084958     0.96   0.339    -.0085292    .0247737
                 5  |   .0035618   .0080846     0.44   0.660    -.0122837    .0194074
                 6  |  -.0004135   .0077838    -0.05   0.958    -.0156696    .0148425
                 7  |  -.0038792   .0075471    -0.51   0.607    -.0186712    .0109128
                 8  |  -.0069009   .0073487    -0.94   0.348    -.0213041    .0075024
                 9  |  -.0095358   .0071768    -1.33   0.184     -.023602    .0045305
                10  |  -.0118337   .0070274    -1.68   0.092    -.0256072    .0019398
                11  |  -.0138382   .0069011    -2.01   0.045     -.027364   -.0003123
                12  |  -.0155869   .0068001    -2.29   0.022    -.0289148   -.0022591
                13  |   -.017113   .0067271    -2.54   0.011    -.0302978   -.0039282
                14  |   -.018445   .0066843    -2.76   0.006    -.0315461    -.005344
                15  |  -.0196081   .0066731    -2.94   0.003    -.0326872    -.006529
                16  |  -.0206239   .0066936    -3.08   0.002    -.0337431   -.0075047
                17  |  -.0215114    .006745    -3.19   0.001    -.0347314   -.0082915
                18  |  -.0222873   .0068256    -3.27   0.001    -.0356652   -.0089094
                19  |  -.0229658   .0069332    -3.31   0.001    -.0365547    -.009377
                20  |  -.0235596   .0070652    -3.33   0.001    -.0374072   -.0097119
                21  |  -.0240795    .007219    -3.34   0.001    -.0382284   -.0099306
                22  |   -.024535   .0073917    -3.32   0.001    -.0390224   -.0100476
                23  |  -.0249346   .0075808    -3.29   0.001    -.0397928   -.0100764
                24  |  -.0252853   .0077842    -3.25   0.001     -.040542   -.0100286
                25  |  -.0255935   .0079996    -3.20   0.001    -.0412725   -.0099146
                26  |  -.0258647   .0082254    -3.14   0.002    -.0419862   -.0097432
                27  |  -.0261036     .00846    -3.09   0.002    -.0426849   -.0095223
                28  |  -.0263144   .0087022    -3.02   0.002    -.0433704   -.0092584
                29  |  -.0265008   .0089509    -2.96   0.003    -.0440442   -.0089573
                30  |  -.0266658   .0092052    -2.90   0.004    -.0447077   -.0086239
                31  |  -.0268122   .0094644    -2.83   0.005    -.0453622   -.0082623
                32  |  -.0269425    .009728    -2.77   0.006     -.046009    -.007876
                33  |  -.0270587   .0099954    -2.71   0.007    -.0466493    -.007468
                34  |  -.0271626   .0102663    -2.65   0.008    -.0472841   -.0070411
                35  |  -.0272558   .0105402    -2.59   0.010    -.0479143   -.0065973
                36  |  -.0273397   .0108171    -2.53   0.011    -.0485408   -.0061387
                37  |  -.0274155   .0110965    -2.47   0.013    -.0491643   -.0056667
                38  |  -.0274843   .0113785    -2.42   0.016    -.0497857   -.0051829
                39  |  -.0275469   .0116627    -2.36   0.018    -.0504053   -.0046884
                40  |  -.0276041   .0119491    -2.31   0.021    -.0510239   -.0041843
                41  |  -.0276567   .0122376    -2.26   0.024    -.0516419   -.0036715
                42  |  -.0277052    .012528    -2.21   0.027    -.0522597   -.0031508
                43  |  -.0277502   .0128203    -2.16   0.030    -.0528776   -.0026229
                44  |  -.0277921   .0131144    -2.12   0.034    -.0534959   -.0020883
                45  |  -.0278314   .0134103    -2.08   0.038     -.054115   -.0015477
                46  |  -.0278683   .0137078    -2.03   0.042     -.054735   -.0010015
                47  |  -.0279032   .0140069    -1.99   0.046    -.0553562   -.0004502
                48  |  -.0279363   .0143076    -1.95   0.051    -.0559786    .0001061
                49  |  -.0279679   .0146098    -1.91   0.056    -.0566025    .0006668
                50  |  -.0279981   .0149134    -1.88   0.060    -.0572279    .0012317
                51  |  -.0280272   .0152185    -1.84   0.066    -.0578549    .0018005
                52  |  -.0280553   .0155249    -1.81   0.071    -.0584836    .0023731
                53  |  -.0280824   .0158327    -1.77   0.076     -.059114    .0029491
                54  |  -.0281089   .0161418    -1.74   0.082    -.0597463    .0035285
                55  |  -.0281347   .0164522    -1.71   0.087    -.0603804     .004111
                56  |  -.0281599   .0167638    -1.68   0.093    -.0610163    .0046966
                57  |  -.0281846   .0170766    -1.65   0.099     -.061654    .0052849
                58  |  -.0282088   .0173905    -1.62   0.105    -.0622937     .005876
                59  |  -.0282327   .0177057    -1.59   0.111    -.0629352    .0064697
                60  |  -.0282563   .0180219    -1.57   0.117    -.0635785     .007066
                61  |  -.0282796   .0183392    -1.54   0.123    -.0642237    .0076646
                62  |  -.0283026   .0186576    -1.52   0.129    -.0648708    .0082655
                63  |  -.0283255    .018977    -1.49   0.136    -.0655197    .0088688
                64  |  -.0283481   .0192974    -1.47   0.142    -.0661704    .0094741
                65  |  -.0283706   .0196189    -1.45   0.148    -.0668229    .0100816
                66  |   -.028393   .0199413    -1.42   0.154    -.0674772    .0106912
                67  |  -.0284153   .0202646    -1.40   0.161    -.0681332    .0113027
                68  |  -.0284374   .0205889    -1.38   0.167     -.068791    .0119161
                69  |  -.0284595   .0209142    -1.36   0.174    -.0694505    .0125315
                70  |  -.0284815   .0212403    -1.34   0.180    -.0701117    .0131487
                71  |  -.0285035   .0215673    -1.32   0.186    -.0707746    .0137677
                72  |  -.0285254   .0218952    -1.30   0.193    -.0714391    .0143884
                73  |  -.0285472   .0222239    -1.28   0.199    -.0721053    .0150109
                74  |  -.0285691   .0225535    -1.27   0.205    -.0727731     .015635
                75  |  -.0285909   .0228839    -1.25   0.212    -.0734426    .0162608
                76  |  -.0286126   .0232152    -1.23   0.218    -.0741136    .0168883
                77  |  -.0286344   .0235472    -1.22   0.224    -.0747862    .0175173
                78  |  -.0286562   .0238801    -1.20   0.230    -.0754603     .018148
                79  |  -.0286779   .0242137    -1.18   0.236    -.0761359    .0187801
                80  |  -.0286996   .0245481    -1.17   0.242    -.0768131    .0194138
                81  |  -.0287214   .0248833    -1.15   0.248    -.0774918     .020049
                82  |  -.0287431   .0252193    -1.14   0.254     -.078172    .0206857
                83  |  -.0287649    .025556    -1.13   0.260    -.0788536    .0213239
                84  |  -.0287866   .0258934    -1.11   0.266    -.0795367    .0219635
                85  |  -.0288084   .0262315    -1.10   0.272    -.0802212    .0226045
                86  |  -.0288301   .0265704    -1.09   0.278    -.0809072     .023247
                87  |  -.0288519   .0269101    -1.07   0.284    -.0815946    .0238909
                88  |  -.0288736   .0272504    -1.06   0.289    -.0822834    .0245361
                89  |  -.0288954   .0275914    -1.05   0.295    -.0829736    .0251827
                90  |  -.0289172   .0279331    -1.04   0.301    -.0836651    .0258307
                91  |   -.028939   .0282755    -1.02   0.306    -.0843581      .02648
                92  |  -.0289608   .0286187    -1.01   0.312    -.0850524    .0271307
                93  |  -.0289827   .0289624    -1.00   0.317     -.085748    .0277827
                94  |  -.0290045   .0293069    -0.99   0.322     -.086445     .028436
                95  |  -.0290264   .0296521    -0.98   0.328    -.0871434    .0290906
                96  |  -.0290482   .0299979    -0.97   0.333     -.087843    .0297465
                97  |  -.0290701   .0303444    -0.96   0.338     -.088544    .0304037
                98  |   -.029092   .0306915    -0.95   0.343    -.0892463    .0310622
                99  |  -.0291139   .0310393    -0.94   0.348    -.0899498    .0317219
               100  |  -.0291359   .0313877    -0.93   0.353    -.0906547     .032383
               101  |  -.0291578   .0317368    -0.92   0.358    -.0913609    .0330452
               102  |  -.0291798   .0320866    -0.91   0.363    -.0920683    .0337088
               103  |  -.0292018    .032437    -0.90   0.368    -.0927771    .0343735
               104  |  -.0292238    .032788    -0.89   0.373     -.093487    .0350395
               105  |  -.0292458   .0331397    -0.88   0.378    -.0941983    .0357068
               106  |  -.0292678   .0334919    -0.87   0.382    -.0949108    .0363752
               107  |  -.0292898   .0338449    -0.87   0.387    -.0956246    .0370449
               108  |  -.0293119   .0341984    -0.86   0.391    -.0963396    .0377158
               109  |   -.029334   .0345526    -0.85   0.396    -.0970558    .0383879
               110  |  -.0293561   .0349074    -0.84   0.400    -.0977733    .0390612
               111  |  -.0293782   .0352628    -0.83   0.405     -.098492    .0397357
               112  |  -.0294003   .0356189    -0.83   0.409     -.099212    .0404114
               113  |  -.0294224   .0359755    -0.82   0.413    -.0999332    .0410883
               114  |  -.0294446   .0363328    -0.81   0.418    -.1006556    .0417664
               115  |  -.0294667   .0366907    -0.80   0.422    -.1013792    .0424457
               116  |  -.0294889   .0370492    -0.80   0.426     -.102104    .0431261
               117  |  -.0295111   .0374083    -0.79   0.430    -.1028301    .0438078
               118  |  -.0295334    .037768    -0.78   0.434    -.1035573    .0444906
               119  |  -.0295556   .0381283    -0.78   0.438    -.1042858    .0451746
               120  |  -.0295779   .0384893    -0.77   0.442    -.1050154    .0458597
               121  |  -.0296001   .0388508    -0.76   0.446    -.1057463     .046546
               122  |  -.0296224   .0392129    -0.76   0.450    -.1064784    .0472335
               123  |  -.0296447   .0395757    -0.75   0.454    -.1072116    .0479222
               124  |   -.029667    .039939    -0.74   0.458     -.107946     .048612
               125  |  -.0296894   .0403029    -0.74   0.461    -.1086817    .0493029
               126  |  -.0297117   .0406675    -0.73   0.465    -.1094185     .049995
               127  |  -.0297341   .0410326    -0.72   0.469    -.1101565    .0506883
               128  |  -.0297565   .0413983    -0.72   0.472    -.1108957    .0513827
               129  |  -.0297789   .0417646    -0.71   0.476    -.1116361    .0520783
               130  |  -.0298013   .0421316    -0.71   0.479    -.1123777     .052775
               131  |  -.0298238   .0424991    -0.70   0.483    -.1131204    .0534729
               132  |  -.0298462   .0428672    -0.70   0.486    -.1138643    .0541719
               133  |  -.0298687   .0432359    -0.69   0.490    -.1146094     .054872
               134  |  -.0298912   .0436051    -0.69   0.493    -.1153557    .0555733
               135  |  -.0299137    .043975    -0.68   0.496    -.1161031    .0562757
               136  |  -.0299362   .0443455    -0.68   0.500    -.1168517    .0569793
               137  |  -.0299588   .0447165    -0.67   0.503    -.1176015     .057684
               138  |  -.0299813   .0450882    -0.66   0.506    -.1183525    .0583899
               139  |  -.0300039   .0454604    -0.66   0.509    -.1191046    .0590968
               140  |  -.0300265   .0458332    -0.66   0.512    -.1198579    .0598049
               141  |  -.0300491   .0462066    -0.65   0.515    -.1206124    .0605142
               142  |  -.0300717   .0465806    -0.65   0.519     -.121368    .0612246
               143  |  -.0300944   .0469552    -0.64   0.522    -.1221248    .0619361
               144  |   -.030117   .0473303    -0.64   0.525    -.1228827    .0626487
               145  |  -.0301397   .0477061    -0.63   0.528    -.1236419    .0633625
               146  |  -.0301624   .0480824    -0.63   0.530    -.1244022    .0640774
               147  |  -.0301851   .0484593    -0.62   0.533    -.1251636    .0647934
               148  |  -.0302078   .0488368    -0.62   0.536    -.1259262    .0655106
               149  |  -.0302306   .0492149    -0.61   0.539      -.12669    .0662289
               150  |  -.0302533   .0495936    -0.61   0.542     -.127455    .0669483
               151  |  -.0302761   .0499728    -0.61   0.545    -.1282211    .0676688
               152  |  -.0302989   .0503527    -0.60   0.547    -.1289883    .0683905
               153  |  -.0303217   .0507331    -0.60   0.550    -.1297568    .0691133
               154  |  -.0303446   .0511141    -0.59   0.553    -.1305263    .0698372
               155  |  -.0303674   .0514957    -0.59   0.555    -.1312971    .0705623
               156  |  -.0303903   .0518779    -0.59   0.558     -.132069    .0712885
               157  |  -.0304131   .0522606    -0.58   0.561    -.1328421    .0720158
               158  |   -.030436    .052644    -0.58   0.563    -.1336163    .0727442
               159  |   -.030459   .0530279    -0.57   0.566    -.1343917    .0734738
               160  |  -.0304819   .0534124    -0.57   0.568    -.1351683    .0742045
               161  |  -.0305049   .0537975    -0.57   0.571     -.135946    .0749363
               162  |  -.0305278   .0541832    -0.56   0.573    -.1367249    .0756692
               163  |  -.0305508   .0545694    -0.56   0.576    -.1375049    .0764033
               164  |  -.0305738   .0549563    -0.56   0.578    -.1382861    .0771385
               165  |  -.0305968   .0553437    -0.55   0.580    -.1390685    .0778748
               166  |  -.0306199   .0557317    -0.55   0.583     -.139852    .0786123
               167  |  -.0306429   .0561203    -0.55   0.585    -.1406367    .0793508
               168  |   -.030666   .0565095    -0.54   0.587    -.1414225    .0800905
               169  |  -.0306891   .0568992    -0.54   0.590    -.1422095    .0808314
               170  |  -.0307122   .0572896    -0.54   0.592    -.1429977    .0815733
               171  |  -.0307353   .0576805    -0.53   0.594     -.143787    .0823164
               172  |  -.0307585    .058072    -0.53   0.596    -.1445775    .0830606
               173  |  -.0307816   .0584641    -0.53   0.599    -.1453692     .083806
               174  |  -.0308048   .0588568    -0.52   0.601     -.146162    .0845524
               175  |   -.030828   .0592501    -0.52   0.603     -.146956       .0853
               176  |  -.0308512   .0596439    -0.52   0.605    -.1477512    .0860488
               177  |  -.0308744   .0600384    -0.51   0.607    -.1485475    .0867986
               178  |  -.0308977   .0604334    -0.51   0.609     -.149345    .0875496
               179  |  -.0309209    .060829    -0.51   0.611    -.1501436    .0883017
               180  |  -.0309442   .0612252    -0.51   0.613    -.1509434     .089055
               181  |  -.0309675    .061622    -0.50   0.615    -.1517444    .0898094
               182  |  -.0309908   .0620194    -0.50   0.617    -.1525466    .0905649
               183  |  -.0310142   .0624173    -0.50   0.619    -.1533499    .0913215
               184  |  -.0310375   .0628159    -0.49   0.621    -.1541544    .0920793
               185  |  -.0310609    .063215    -0.49   0.623      -.15496    .0928382
               186  |  -.0310843   .0636147    -0.49   0.625    -.1557669    .0935983
               187  |  -.0311077    .064015    -0.49   0.627    -.1565749    .0943595
               188  |  -.0311311   .0644159    -0.48   0.629     -.157384    .0951218
               189  |  -.0311546   .0648174    -0.48   0.631    -.1581944    .0958852
               190  |   -.031178   .0652195    -0.48   0.633    -.1590059    .0966498
               191  |  -.0312015   .0656222    -0.48   0.634    -.1598186    .0974156
               192  |   -.031225   .0660254    -0.47   0.636    -.1606324    .0981825
               193  |  -.0312485   .0664293    -0.47   0.638    -.1614475    .0989505
               194  |   -.031272   .0668337    -0.47   0.640    -.1622637    .0997196
               195  |  -.0312956   .0672387    -0.47   0.642    -.1630811    .1004899
               196  |  -.0313191   .0676444    -0.46   0.643    -.1638996    .1012614
               197  |  -.0313427   .0680506    -0.46   0.645    -.1647194    .1020339
-------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. marginsplot, ///
>         recast(line) plot1opts(lcolor(gs8)) ciopt(color(black%20)) recastci(rarea) ///
>         ytitle("AME", size(medsmall)) ///
>         xtitle("Troops", size(medsmall))  ///
>         title("Anocracy", size(medium)) yline(0, lcolor(red)) ///
>         addplot(hist troops, /// adding histogram marginsplot graph
> blcolor(black%30) fcolor(white%10) /// bar line and fill colors
> percent /// histogram bins in "percent" rather than "discrete" (actual)
> yaxis(2) /// calls for 2nd y-axis
> yscale(alt lcolor() axis(2)) /// scaling on 2nd y-axis
> ylabel(0 "0%" 20 "20%" 40 "40%" 60 "60%" 80 "80%" 100 "100%", /// labels on 2nd y-axis
> labcolor() axis(2) tlcolor(black) tlwidth(thin) labsize(small)) /// label options on 2nd y-axis
> ytitle(" ", axis(2)))  legend(off)

Variables that uniquely identify margins: troops

. graph save "figures\Study_2\B2.4.gph", replace
file figures\Study_2\B2.4.gph saved

. 
. 
. *model 8a: New democracy
. eststo: poisson milcoupsum new_robust_democracy6##c.troops population2000 gdpcap durable conflict milexp exsol, cluster(ccode)

Iteration 0:   log pseudolikelihood = -411.78171  
Iteration 1:   log pseudolikelihood = -406.87226  
Iteration 2:   log pseudolikelihood = -403.34415  
Iteration 3:   log pseudolikelihood = -400.72867  
Iteration 4:   log pseudolikelihood = -400.69906  
Iteration 5:   log pseudolikelihood = -400.69903  

Poisson regression                                      Number of obs =  3,001
                                                        Wald chi2(9)  =  72.73
Log pseudolikelihood = -400.69903                       Prob > chi2   = 0.0000

                                                  (Std. err. adjusted for 148 clusters in ccode)
------------------------------------------------------------------------------------------------
                               |               Robust
                    milcoupsum | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------------------------+----------------------------------------------------------------
       1.new_robust_democracy6 |  -1.118677   .4094854    -2.73   0.006    -1.921254   -.3161008
                        troops |  -.1720984   .3543506    -0.49   0.627    -.8666128    .5224159
                               |
new_robust_democracy6#c.troops |
                            1  |  -.6602458   .7434278    -0.89   0.374    -2.117338     .796846
                               |
                population2000 |  -3.33e-10   6.06e-09    -0.05   0.956    -1.22e-08    1.15e-08
                        gdpcap |   -.007618   .0313899    -0.24   0.808    -.0691412    .0539051
                       durable |  -.0167517    .021227    -0.79   0.430    -.0583558    .0248523
                      conflict |   .8082747   .3447763     2.34   0.019     .1325255    1.484024
                        milexp |  -398.4421   171.2426    -2.33   0.020    -734.0715   -62.81271
                         exsol |   .7746237   2.123611     0.36   0.715    -3.387578    4.936825
                         _cons |  -2.430278    .319332    -7.61   0.000    -3.056157   -1.804399
------------------------------------------------------------------------------------------------
(est14 stored)

. margins, dydx(new_robust_democracy6) at (troops =  (0(0.05)9.8)) post

Average marginal effects                                 Number of obs = 3,001
Model VCE: Robust

Expression: Predicted number of events, predict()
dy/dx wrt:  1.new_robust_democracy6
1._at:   troops =    0
2._at:   troops =  .05
3._at:   troops =   .1
4._at:   troops =  .15
5._at:   troops =   .2
6._at:   troops =  .25
7._at:   troops =   .3
8._at:   troops =  .35
9._at:   troops =   .4
10._at:  troops =  .45
11._at:  troops =   .5
12._at:  troops =  .55
13._at:  troops =   .6
14._at:  troops =  .65
15._at:  troops =   .7
16._at:  troops =  .75
17._at:  troops =   .8
18._at:  troops =  .85
19._at:  troops =   .9
20._at:  troops =  .95
21._at:  troops =    1
22._at:  troops = 1.05
23._at:  troops =  1.1
24._at:  troops = 1.15
25._at:  troops =  1.2
26._at:  troops = 1.25
27._at:  troops =  1.3
28._at:  troops = 1.35
29._at:  troops =  1.4
30._at:  troops = 1.45
31._at:  troops =  1.5
32._at:  troops = 1.55
33._at:  troops =  1.6
34._at:  troops = 1.65
35._at:  troops =  1.7
36._at:  troops = 1.75
37._at:  troops =  1.8
38._at:  troops = 1.85
39._at:  troops =  1.9
40._at:  troops = 1.95
41._at:  troops =    2
42._at:  troops = 2.05
43._at:  troops =  2.1
44._at:  troops = 2.15
45._at:  troops =  2.2
46._at:  troops = 2.25
47._at:  troops =  2.3
48._at:  troops = 2.35
49._at:  troops =  2.4
50._at:  troops = 2.45
51._at:  troops =  2.5
52._at:  troops = 2.55
53._at:  troops =  2.6
54._at:  troops = 2.65
55._at:  troops =  2.7
56._at:  troops = 2.75
57._at:  troops =  2.8
58._at:  troops = 2.85
59._at:  troops =  2.9
60._at:  troops = 2.95
61._at:  troops =    3
62._at:  troops = 3.05
63._at:  troops =  3.1
64._at:  troops = 3.15
65._at:  troops =  3.2
66._at:  troops = 3.25
67._at:  troops =  3.3
68._at:  troops = 3.35
69._at:  troops =  3.4
70._at:  troops = 3.45
71._at:  troops =  3.5
72._at:  troops = 3.55
73._at:  troops =  3.6
74._at:  troops = 3.65
75._at:  troops =  3.7
76._at:  troops = 3.75
77._at:  troops =  3.8
78._at:  troops = 3.85
79._at:  troops =  3.9
80._at:  troops = 3.95
81._at:  troops =    4
82._at:  troops = 4.05
83._at:  troops =  4.1
84._at:  troops = 4.15
85._at:  troops =  4.2
86._at:  troops = 4.25
87._at:  troops =  4.3
88._at:  troops = 4.35
89._at:  troops =  4.4
90._at:  troops = 4.45
91._at:  troops =  4.5
92._at:  troops = 4.55
93._at:  troops =  4.6
94._at:  troops = 4.65
95._at:  troops =  4.7
96._at:  troops = 4.75
97._at:  troops =  4.8
98._at:  troops = 4.85
99._at:  troops =  4.9
100._at: troops = 4.95
101._at: troops =    5
102._at: troops = 5.05
103._at: troops =  5.1
104._at: troops = 5.15
105._at: troops =  5.2
106._at: troops = 5.25
107._at: troops =  5.3
108._at: troops = 5.35
109._at: troops =  5.4
110._at: troops = 5.45
111._at: troops =  5.5
112._at: troops = 5.55
113._at: troops =  5.6
114._at: troops = 5.65
115._at: troops =  5.7
116._at: troops = 5.75
117._at: troops =  5.8
118._at: troops = 5.85
119._at: troops =  5.9
120._at: troops = 5.95
121._at: troops =    6
122._at: troops = 6.05
123._at: troops =  6.1
124._at: troops = 6.15
125._at: troops =  6.2
126._at: troops = 6.25
127._at: troops =  6.3
128._at: troops = 6.35
129._at: troops =  6.4
130._at: troops = 6.45
131._at: troops =  6.5
132._at: troops = 6.55
133._at: troops =  6.6
134._at: troops = 6.65
135._at: troops =  6.7
136._at: troops = 6.75
137._at: troops =  6.8
138._at: troops = 6.85
139._at: troops =  6.9
140._at: troops = 6.95
141._at: troops =    7
142._at: troops = 7.05
143._at: troops =  7.1
144._at: troops = 7.15
145._at: troops =  7.2
146._at: troops = 7.25
147._at: troops =  7.3
148._at: troops = 7.35
149._at: troops =  7.4
150._at: troops = 7.45
151._at: troops =  7.5
152._at: troops = 7.55
153._at: troops =  7.6
154._at: troops = 7.65
155._at: troops =  7.7
156._at: troops = 7.75
157._at: troops =  7.8
158._at: troops = 7.85
159._at: troops =  7.9
160._at: troops = 7.95
161._at: troops =    8
162._at: troops = 8.05
163._at: troops =  8.1
164._at: troops = 8.15
165._at: troops =  8.2
166._at: troops = 8.25
167._at: troops =  8.3
168._at: troops = 8.35
169._at: troops =  8.4
170._at: troops = 8.45
171._at: troops =  8.5
172._at: troops = 8.55
173._at: troops =  8.6
174._at: troops = 8.65
175._at: troops =  8.7
176._at: troops = 8.75
177._at: troops =  8.8
178._at: troops = 8.85
179._at: troops =  8.9
180._at: troops = 8.95
181._at: troops =    9
182._at: troops = 9.05
183._at: troops =  9.1
184._at: troops = 9.15
185._at: troops =  9.2
186._at: troops = 9.25
187._at: troops =  9.3
188._at: troops = 9.35
189._at: troops =  9.4
190._at: troops = 9.45
191._at: troops =  9.5
192._at: troops = 9.55
193._at: troops =  9.6
194._at: troops = 9.65
195._at: troops =  9.7
196._at: troops = 9.75
197._at: troops =  9.8

------------------------------------------------------------------------------------------
                         |            Delta-method
                         |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------------------+----------------------------------------------------------------
0.new_robust_democracy6  |  (base outcome)
-------------------------+----------------------------------------------------------------
1.new_robust_democracy6  |
                     _at |
                      1  |  -.0334746   .0101015    -3.31   0.001    -.0532733    -.013676
                      2  |  -.0337108   .0096425    -3.50   0.000    -.0526098   -.0148118
                      3  |  -.0339236   .0092188    -3.68   0.000    -.0519921   -.0158551
                      4  |  -.0341141   .0088303    -3.86   0.000    -.0514212    -.016807
                      5  |  -.0342834   .0084779    -4.04   0.000    -.0508997   -.0176671
                      6  |  -.0344325    .008163    -4.22   0.000    -.0504317   -.0184332
                      7  |  -.0345622    .007888    -4.38   0.000    -.0500223    -.019102
                      8  |  -.0346734   .0076553    -4.53   0.000    -.0496776   -.0196692
                      9  |  -.0347671    .007468    -4.66   0.000    -.0494042   -.0201301
                     10  |  -.0348441   .0073288    -4.75   0.000    -.0492083   -.0204799
                     11  |  -.0349052   .0072401    -4.82   0.000    -.0490956   -.0207148
                     12  |   -.034951   .0072037    -4.85   0.000    -.0490701   -.0208319
                     13  |  -.0349824   .0072204    -4.84   0.000    -.0491342   -.0208306
                     14  |  -.0350001   .0072899    -4.80   0.000    -.0492881    -.020712
                     15  |  -.0350046   .0074109    -4.72   0.000    -.0495297   -.0204796
                     16  |  -.0349968   .0075809    -4.62   0.000     -.049855   -.0201385
                     17  |   -.034977   .0077967    -4.49   0.000    -.0502582   -.0196958
                     18  |  -.0349461   .0080546    -4.34   0.000    -.0507327   -.0191594
                     19  |  -.0349044   .0083505    -4.18   0.000     -.051271   -.0185378
                     20  |  -.0348526   .0086802    -4.02   0.000    -.0518655   -.0178397
                     21  |  -.0347911   .0090397    -3.85   0.000    -.0525087   -.0170736
                     22  |  -.0347205   .0094251    -3.68   0.000    -.0531935   -.0162476
                     23  |  -.0346413   .0098329    -3.52   0.000    -.0539134   -.0153692
                     24  |  -.0345538   .0102597    -3.37   0.001    -.0546625   -.0144452
                     25  |  -.0344586   .0107026    -3.22   0.001    -.0554352   -.0134819
                     26  |  -.0343559   .0111589    -3.08   0.002     -.056227   -.0124849
                     27  |  -.0342463   .0116263    -2.95   0.003    -.0570334   -.0114592
                     28  |  -.0341301   .0121026    -2.82   0.005    -.0578509   -.0104094
                     29  |  -.0340077   .0125861    -2.70   0.007     -.058676   -.0093394
                     30  |  -.0338794    .013075    -2.59   0.010    -.0595059   -.0082529
                     31  |  -.0337456   .0135679    -2.49   0.013    -.0603381    -.007153
                     32  |  -.0336065   .0140635    -2.39   0.017    -.0611704   -.0060426
                     33  |  -.0334625   .0145606    -2.30   0.022    -.0620007   -.0049243
                     34  |  -.0333139   .0150581    -2.21   0.027    -.0628273   -.0038005
                     35  |  -.0331609   .0155553    -2.13   0.033    -.0636487   -.0026731
                     36  |  -.0330038   .0160512    -2.06   0.040    -.0644635   -.0015441
                     37  |  -.0328429   .0165451    -1.99   0.047    -.0652706   -.0004151
                     38  |  -.0326784   .0170363    -1.92   0.055     -.066069    .0007122
                     39  |  -.0325105   .0175244    -1.86   0.064    -.0668576    .0018366
                     40  |  -.0323395   .0180086    -1.80   0.073    -.0676357    .0029567
                     41  |  -.0321656   .0184886    -1.74   0.082    -.0684027    .0040714
                     42  |   -.031989    .018964    -1.69   0.092    -.0691577    .0051797
                     43  |  -.0318098   .0194343    -1.64   0.102    -.0699004    .0062807
                     44  |  -.0316284   .0198992    -1.59   0.112    -.0706302    .0073734
                     45  |  -.0314447   .0203585    -1.54   0.122    -.0713467    .0084572
                     46  |  -.0312591   .0208118    -1.50   0.133    -.0720495    .0095313
                     47  |  -.0310716    .021259    -1.46   0.144    -.0727384    .0105951
                     48  |  -.0308825   .0216997    -1.42   0.155    -.0734132    .0116481
                     49  |  -.0306919   .0221339    -1.39   0.166    -.0740734    .0126897
                     50  |  -.0304999   .0225613    -1.35   0.176    -.0747191    .0137194
                     51  |  -.0303066   .0229818    -1.32   0.187    -.0753501    .0147369
                     52  |  -.0301122   .0233953    -1.29   0.198    -.0759662    .0157418
                     53  |  -.0299168   .0238018    -1.26   0.209    -.0765674    .0167338
                     54  |  -.0297205    .024201    -1.23   0.219    -.0771536    .0177125
                     55  |  -.0295235    .024593    -1.20   0.230    -.0777248    .0186778
                     56  |  -.0293258   .0249776    -1.17   0.240     -.078281    .0196294
                     57  |  -.0291276   .0253549    -1.15   0.251    -.0788222     .020567
                     58  |  -.0289289   .0257247    -1.12   0.261    -.0793484    .0214906
                     59  |  -.0287298   .0260871    -1.10   0.271    -.0798597    .0224001
                     60  |  -.0285304   .0264421    -1.08   0.281    -.0803561    .0232952
                     61  |  -.0283309   .0267897    -1.06   0.290    -.0808377     .024176
                     62  |  -.0281312   .0271298    -1.04   0.300    -.0813046    .0250423
                     63  |  -.0279314   .0274625    -1.02   0.309    -.0817569     .025894
                     64  |  -.0277317   .0277878    -1.00   0.318    -.0821948    .0267313
                     65  |  -.0275321   .0281057    -0.98   0.327    -.0826182     .027554
                     66  |  -.0273327   .0284162    -0.96   0.336    -.0830275    .0283621
                     67  |  -.0271335   .0287195    -0.94   0.345    -.0834226    .0291557
                     68  |  -.0269345   .0290155    -0.93   0.353    -.0838038    .0299347
                     69  |  -.0267359   .0293042    -0.91   0.362    -.0841712    .0306993
                     70  |  -.0265377   .0295858    -0.90   0.370    -.0845249    .0314495
                     71  |    -.02634   .0298603    -0.88   0.378    -.0848652    .0321853
                     72  |  -.0261427   .0301278    -0.87   0.386    -.0851921    .0329067
                     73  |  -.0259459   .0303883    -0.85   0.393    -.0855058     .033614
                     74  |  -.0257497   .0306418    -0.84   0.401    -.0858066    .0343072
                     75  |  -.0255542   .0308885    -0.83   0.408    -.0860946    .0349863
                     76  |  -.0253593   .0311285    -0.81   0.415      -.08637    .0356514
                     77  |  -.0251651   .0313617    -0.80   0.422    -.0866329    .0363027
                     78  |  -.0249716   .0315883    -0.79   0.429    -.0868835    .0369404
                     79  |  -.0247788   .0318083    -0.78   0.436     -.087122    .0375644
                     80  |  -.0245869   .0320219    -0.77   0.443    -.0873487    .0381749
                     81  |  -.0243957   .0322291    -0.76   0.449    -.0875636    .0387721
                     82  |  -.0242055   .0324299    -0.75   0.455     -.087767    .0393561
                     83  |   -.024016   .0326246    -0.74   0.462     -.087959    .0399269
                     84  |  -.0238275    .032813    -0.73   0.468    -.0881399    .0404849
                     85  |  -.0236399   .0329954    -0.72   0.474    -.0883098      .04103
                     86  |  -.0234532   .0331719    -0.71   0.480    -.0884688    .0415625
                     87  |  -.0232674   .0333424    -0.70   0.485    -.0886173    .0420824
                     88  |  -.0230827   .0335071    -0.69   0.491    -.0887554      .04259
                     89  |  -.0228989    .033666    -0.68   0.496    -.0888831    .0430853
                     90  |  -.0227161   .0338194    -0.67   0.502    -.0890009    .0435686
                     91  |  -.0225344   .0339671    -0.66   0.507    -.0891087    .0440399
                     92  |  -.0223537   .0341094    -0.66   0.512    -.0892068    .0444995
                     93  |   -.022174   .0342462    -0.65   0.517    -.0892954    .0449474
                     94  |  -.0219954   .0343778    -0.64   0.522    -.0893746    .0453839
                     95  |  -.0218178   .0345041    -0.63   0.527    -.0894446     .045809
                     96  |  -.0216413   .0346253    -0.63   0.532    -.0895056     .046223
                     97  |  -.0214659   .0347414    -0.62   0.537    -.0895578    .0466259
                     98  |  -.0212917   .0348525    -0.61   0.541    -.0896013     .047018
                     99  |  -.0211185   .0349587    -0.60   0.546    -.0896363    .0473994
                    100  |  -.0209464   .0350601    -0.60   0.550    -.0896629    .0477702
                    101  |  -.0207754   .0351567    -0.59   0.555    -.0896813    .0481305
                    102  |  -.0206055   .0352487    -0.58   0.559    -.0896917    .0484807
                    103  |  -.0204368   .0353361    -0.58   0.563    -.0896943    .0488207
                    104  |  -.0202692    .035419    -0.57   0.567    -.0896891    .0491507
                    105  |  -.0201027   .0354974    -0.57   0.571    -.0896764     .049471
                    106  |  -.0199374   .0355715    -0.56   0.575    -.0896563    .0497815
                    107  |  -.0197732   .0356413    -0.55   0.579    -.0896289    .0500826
                    108  |  -.0196101    .035707    -0.55   0.583    -.0895945    .0503742
                    109  |  -.0194482   .0357684    -0.54   0.587     -.089553    .0506567
                    110  |  -.0192874   .0358259    -0.54   0.590    -.0895048      .05093
                    111  |  -.0191277   .0358793    -0.53   0.594    -.0894498    .0511944
                    112  |  -.0189692   .0359288    -0.53   0.598    -.0893884    .0514499
                    113  |  -.0188118   .0359745    -0.52   0.601    -.0893205    .0516968
                    114  |  -.0186556   .0360164    -0.52   0.604    -.0892464    .0519352
                    115  |  -.0185005   .0360546    -0.51   0.608    -.0891662    .0521651
                    116  |  -.0183466   .0360891    -0.51   0.611    -.0890799    .0523868
                    117  |  -.0181937   .0361201    -0.50   0.614    -.0889878    .0526004
                    118  |   -.018042   .0361476    -0.50   0.618      -.08889    .0528059
                    119  |  -.0178915   .0361716    -0.49   0.621    -.0887865    .0530036
                    120  |   -.017742   .0361923    -0.49   0.624    -.0886776    .0531935
                    121  |  -.0175937   .0362096    -0.49   0.627    -.0885632    .0533758
                    122  |  -.0174465   .0362237    -0.48   0.630    -.0884437    .0535507
                    123  |  -.0173004   .0362346    -0.48   0.633    -.0883189    .0537181
                    124  |  -.0171555   .0362424    -0.47   0.636    -.0881892    .0538783
                    125  |  -.0170116   .0362471    -0.47   0.639    -.0880546    .0540314
                    126  |  -.0168688   .0362488    -0.47   0.642    -.0879151    .0541774
                    127  |  -.0167272   .0362475    -0.46   0.644     -.087771    .0543166
                    128  |  -.0165866   .0362434    -0.46   0.647    -.0876223     .054449
                    129  |  -.0164472   .0362364    -0.45   0.650    -.0874691    .0545748
                    130  |  -.0163088   .0362266    -0.45   0.653    -.0873116     .054694
                    131  |  -.0161715   .0362141    -0.45   0.655    -.0871497    .0548068
                    132  |  -.0160353   .0361989    -0.44   0.658    -.0869838    .0549132
                    133  |  -.0159001   .0361811    -0.44   0.660    -.0868137    .0550135
                    134  |   -.015766   .0361607    -0.44   0.663    -.0866397    .0551076
                    135  |   -.015633   .0361378    -0.43   0.665    -.0864618    .0551957
                    136  |  -.0155011   .0361124    -0.43   0.668    -.0862801     .055278
                    137  |  -.0153701   .0360846    -0.43   0.670    -.0860947    .0553544
                    138  |  -.0152403   .0360545    -0.42   0.673    -.0859057    .0554252
                    139  |  -.0151114    .036022    -0.42   0.675    -.0857132    .0554903
                    140  |  -.0149836   .0359872    -0.42   0.677    -.0855173      .05555
                    141  |  -.0148569   .0359502    -0.41   0.679     -.085318    .0556043
                    142  |  -.0147311    .035911    -0.41   0.682    -.0851155    .0556532
                    143  |  -.0146064   .0358697    -0.41   0.684    -.0849098     .055697
                    144  |  -.0144826   .0358263    -0.40   0.686     -.084701    .0557357
                    145  |  -.0143599   .0357809    -0.40   0.688    -.0844892    .0557693
                    146  |  -.0142382   .0357334    -0.40   0.690    -.0842744     .055798
                    147  |  -.0141175    .035684    -0.40   0.692    -.0840568    .0558219
                    148  |  -.0139977   .0356326    -0.39   0.694    -.0838364     .055841
                    149  |  -.0138789   .0355794    -0.39   0.696    -.0836133    .0558554
                    150  |  -.0137611   .0355243    -0.39   0.698    -.0833875    .0558653
                    151  |  -.0136443   .0354675    -0.38   0.700    -.0831592    .0558707
                    152  |  -.0135284   .0354088    -0.38   0.702    -.0829284    .0558716
                    153  |  -.0134134   .0353484    -0.38   0.704    -.0826951    .0558682
                    154  |  -.0132994   .0352864    -0.38   0.706    -.0824595    .0558606
                    155  |  -.0131864   .0352227    -0.37   0.708    -.0822216    .0558488
                    156  |  -.0130743   .0351574    -0.37   0.710    -.0819815    .0558329
                    157  |  -.0129631   .0350905    -0.37   0.712    -.0817391     .055813
                    158  |  -.0128528    .035022    -0.37   0.714    -.0814947    .0557891
                    159  |  -.0127434   .0349521    -0.36   0.715    -.0812483    .0557614
                    160  |   -.012635   .0348807    -0.36   0.717    -.0809998    .0557299
                    161  |  -.0125274   .0348078    -0.36   0.719    -.0807495    .0556946
                    162  |  -.0124208   .0347335    -0.36   0.721    -.0804972    .0556557
                    163  |   -.012315   .0346579    -0.36   0.722    -.0802432    .0556132
                    164  |  -.0122101   .0345809    -0.35   0.724    -.0799874    .0555672
                    165  |  -.0121061   .0345026    -0.35   0.726    -.0797299    .0555178
                    166  |  -.0120029    .034423    -0.35   0.727    -.0794708     .055465
                    167  |  -.0119006   .0343422    -0.35   0.729    -.0792101    .0554088
                    168  |  -.0117992   .0342601    -0.34   0.731    -.0789478    .0553495
                    169  |  -.0116986   .0341769    -0.34   0.732     -.078684    .0552869
                    170  |  -.0115988   .0340925    -0.34   0.734    -.0784189    .0552212
                    171  |  -.0114999   .0340069    -0.34   0.735    -.0781523    .0551525
                    172  |  -.0114018   .0339203    -0.34   0.737    -.0778844    .0550807
                    173  |  -.0113045   .0338326    -0.33   0.738    -.0776152    .0550061
                    174  |  -.0112081   .0337438    -0.33   0.740    -.0773447    .0549285
                    175  |  -.0111125    .033654    -0.33   0.741    -.0770731    .0548481
                    176  |  -.0110176   .0335632    -0.33   0.743    -.0768002     .054765
                    177  |  -.0109236   .0334714    -0.33   0.744    -.0765263    .0546792
                    178  |  -.0108303   .0333787    -0.32   0.746    -.0762513    .0545907
                    179  |  -.0107379    .033285    -0.32   0.747    -.0759753    .0544996
                    180  |  -.0106462   .0331905    -0.32   0.748    -.0756984     .054406
                    181  |  -.0105553   .0330951    -0.32   0.750    -.0754204    .0543099
                    182  |  -.0104651   .0329988    -0.32   0.751    -.0751416    .0542113
                    183  |  -.0103757   .0329017    -0.32   0.752    -.0748619    .0541104
                    184  |  -.0102871   .0328038    -0.31   0.754    -.0745814    .0540071
                    185  |  -.0101992   .0327051    -0.31   0.755    -.0743001    .0539016
                    186  |  -.0101121   .0326057    -0.31   0.756     -.074018    .0537938
                    187  |  -.0100257   .0325055    -0.31   0.758    -.0737352    .0536839
                    188  |    -.00994   .0324046    -0.31   0.759    -.0734518    .0535718
                    189  |  -.0098551    .032303    -0.31   0.760    -.0731677    .0534576
                    190  |  -.0097708   .0322007    -0.30   0.762     -.072883    .0533414
                    191  |  -.0096873   .0320978    -0.30   0.763    -.0725978    .0532231
                    192  |  -.0096045   .0319942    -0.30   0.764     -.072312     .053103
                    193  |  -.0095224     .03189    -0.30   0.765    -.0720257    .0529809
                    194  |   -.009441   .0317852    -0.30   0.766    -.0717389    .0528569
                    195  |  -.0093603   .0316799    -0.30   0.768    -.0714517    .0527312
                    196  |  -.0092802    .031574    -0.29   0.769    -.0711641    .0526036
                    197  |  -.0092009   .0314675    -0.29   0.770    -.0708761    .0524743
------------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. marginsplot, ///
>         recast(line) plot1opts(lcolor(gs8)) ciopt(color(black%20)) recastci(rarea) ///
>         ytitle("AME", size(medsmall)) ///
>         xtitle("Troops", size(medsmall))  ///
>         title("New democracy", size(medium)) yline(0, lcolor(red)) ///
>         addplot(hist troops, /// adding histogram marginsplot graph
> blcolor(black%30) fcolor(white%10) /// bar line and fill colors
> percent /// histogram bins in "percent" rather than "discrete" (actual)
> yaxis(2) /// calls for 2nd y-axis
> yscale(alt lcolor() axis(2)) /// scaling on 2nd y-axis
> ylabel(0 "0%" 20 "20%" 40 "40%" 60 "60%" 80 "80%" 100 "100%", /// labels on 2nd y-axis
> labcolor() axis(2) tlcolor(black) tlwidth(thin) labsize(small)) /// label options on 2nd y-axis
> ytitle(" ", axis(2)))  legend(off)

Variables that uniquely identify margins: troops

. graph save "figures\Study_2\B2.8.gph", replace
file figures\Study_2\B2.8.gph saved

. 
. 
. graph combine "figures\Study_2\B2.2.gph" "figures\Study_2\B2.4.gph" "figures\Study_2\B2.8.gph", col(2) 

. graph save "figures\Study_2\Figure_B3_AME_by_troop_rounded.gph", as(png) name("Graph") replace
option as() not allowed
r(198);

end of do-file

r(198);

. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. graph combine "figures\Study_2\B2.2.gph" "figures\Study_2\B2.4.gph" "figures\Study_2\B2.8.gph", col(2) 

. graph save "figures\Study_2\Figure_B3_AME_by_troop_rounded.gph", replace
file figures\Study_2\Figure_B3_AME_by_troop_rounded.gph saved

. 
end of do-file

. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. graph combine "figures\Study_2\Figure_2_original.gph" "figures\Study_2\Figure_2_AME_Regime_Type.gph" "figures\Study_2\Figure_B3_AME_by_troop_rounded.gph", col(2) 

. 
end of do-file

. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. 
. graph combine "figures\Study_2\B2.2.gph" "figures\Study_2\B2.4.gph" "figures\Study_2\B2.8.gph", col(1) 

. graph save "figures\Study_2\Figure_B3_AME_by_troop_rounded.gph", replace
file figures\Study_2\Figure_B3_AME_by_troop_rounded.gph saved

. 
end of do-file

. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. 
. graph combine "figures\Study_2\B2.2.gph" "figures\Study_2\B2.4.gph" "figures\Study_2\B2.8.gph", col(3) 

. graph save "figures\Study_2\Figure_B3_AME_by_troop_rounded.gph", replace
file figures\Study_2\Figure_B3_AME_by_troop_rounded.gph saved

. 
end of do-file

. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. 
. 
. graph combine "figures\Study_2\Figure_2_original.gph" "figures\Study_2\Figure_2_AME_Regime_Type.gph", col(2)

. graph save "figures\Study_2\Figure_work_around.gph", replace
(file figures\Study_2\Figure_work_around.gph not found)
file figures\Study_2\Figure_work_around.gph saved

. 
end of do-file

. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. 
. *------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------*
. * Correctly estimated AME
. *------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------*
.         
. *model 2a: autocracy
. poisson milcoupsum robust_autocracy6##c.troops population2000 gdpcap durable conflict milexp exsol, cluster(ccode)

Iteration 0:   log pseudolikelihood = -449.22112  
Iteration 1:   log pseudolikelihood = -410.06911  
Iteration 2:   log pseudolikelihood = -404.01836  
Iteration 3:   log pseudolikelihood = -402.11343  
Iteration 4:   log pseudolikelihood = -402.01812  
Iteration 5:   log pseudolikelihood = -402.01764  
Iteration 6:   log pseudolikelihood = -402.01764  

Poisson regression                                      Number of obs =  3,001
                                                        Wald chi2(9)  =  82.80
Log pseudolikelihood = -402.01764                       Prob > chi2   = 0.0000

                                              (Std. err. adjusted for 148 clusters in ccode)
--------------------------------------------------------------------------------------------
                           |               Robust
                milcoupsum | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------------+----------------------------------------------------------------
       1.robust_autocracy6 |   .5278635   .3128468     1.69   0.092    -.0853049    1.141032
                    troops |  -1.838259   1.027145    -1.79   0.074    -3.851427    .1749095
                           |
robust_autocracy6#c.troops |
                        1  |   2.053367   1.009086     2.03   0.042     .0755952    4.031138
                           |
            population2000 |  -2.34e-09   6.43e-09    -0.36   0.716    -1.49e-08    1.03e-08
                    gdpcap |   -.020064   .0477717    -0.42   0.674    -.1136947    .0735667
                   durable |  -.0224258   .0221588    -1.01   0.312    -.0658563    .0210046
                  conflict |   .9358275   .3751772     2.49   0.013     .2004936    1.671161
                    milexp |  -378.6024   181.1369    -2.09   0.037    -733.6242   -23.58055
                     exsol |   .9254581   2.147148     0.43   0.666    -3.282875    5.133792
                     _cons |  -2.714514   .3781303    -7.18   0.000    -3.455636   -1.973392
--------------------------------------------------------------------------------------------

. margins, dydx(robust_autocracy6) post

Average marginal effects                                 Number of obs = 3,001
Model VCE: Robust

Expression: Predicted number of events, predict()
dy/dx wrt:  1.robust_autocracy6

-------------------------------------------------------------------------------------
                    |            Delta-method
                    |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
--------------------+----------------------------------------------------------------
1.robust_autocracy6 |   .0325869   .0163034     2.00   0.046     .0006328     .064541
-------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. est store m2a

. 
. 
. *model 4a: anocracy
. poisson milcoupsum robust_anocracy5##c.troops population2000 gdpcap durable conflict milexp exsol, cluster(ccode)

Iteration 0:   log pseudolikelihood = -418.40873  
Iteration 1:   log pseudolikelihood =  -412.0033  
Iteration 2:   log pseudolikelihood =   -407.598  
Iteration 3:   log pseudolikelihood = -405.16414  
Iteration 4:   log pseudolikelihood = -405.09412  
Iteration 5:   log pseudolikelihood = -405.09351  
Iteration 6:   log pseudolikelihood = -405.09351  

Poisson regression                                      Number of obs =  3,001
                                                        Wald chi2(9)  =  47.18
Log pseudolikelihood = -405.09351                       Prob > chi2   = 0.0000

                                             (Std. err. adjusted for 148 clusters in ccode)
-------------------------------------------------------------------------------------------
                          |               Robust
               milcoupsum | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------------------+----------------------------------------------------------------
       1.robust_anocracy5 |   .6782844   .2786318     2.43   0.015     .1321761    1.224393
                   troops |   .0150535   .2278477     0.07   0.947    -.4315197    .4616267
                          |
robust_anocracy5#c.troops |
                       1  |  -2.774543   .7840732    -3.54   0.000    -4.311299   -1.237788
                          |
           population2000 |  -4.52e-10   5.94e-09    -0.08   0.939    -1.21e-08    1.12e-08
                   gdpcap |  -.0128145   .0410951    -0.31   0.755    -.0933595    .0677304
                  durable |  -.0147644     .02168    -0.68   0.496    -.0572563    .0277276
                 conflict |   .9400657   .3531598     2.66   0.008     .2478853    1.632246
                   milexp |  -426.8629   172.5747    -2.47   0.013    -765.1032   -88.62272
                    exsol |   1.080705   2.444078     0.44   0.658    -3.709601     5.87101
                    _cons |  -3.068939   .3479924    -8.82   0.000    -3.750991   -2.386886
-------------------------------------------------------------------------------------------

. margins, dydx(robust_anocracy5) post

Average marginal effects                                 Number of obs = 3,001
Model VCE: Robust

Expression: Predicted number of events, predict()
dy/dx wrt:  1.robust_anocracy5

------------------------------------------------------------------------------------
                   |            Delta-method
                   |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------------+----------------------------------------------------------------
1.robust_anocracy5 |   .0183265   .0096998     1.89   0.059    -.0006847    .0373377
------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. est store m4a

. 
. 
. *model 8a: New democracy
. eststo: poisson milcoupsum new_robust_democracy6##c.troops population2000 gdpcap durable conflict milexp exsol, cluster(ccode)

Iteration 0:   log pseudolikelihood = -411.78171  
Iteration 1:   log pseudolikelihood = -406.87226  
Iteration 2:   log pseudolikelihood = -403.34415  
Iteration 3:   log pseudolikelihood = -400.72867  
Iteration 4:   log pseudolikelihood = -400.69906  
Iteration 5:   log pseudolikelihood = -400.69903  

Poisson regression                                      Number of obs =  3,001
                                                        Wald chi2(9)  =  72.73
Log pseudolikelihood = -400.69903                       Prob > chi2   = 0.0000

                                                  (Std. err. adjusted for 148 clusters in ccode)
------------------------------------------------------------------------------------------------
                               |               Robust
                    milcoupsum | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------------------------+----------------------------------------------------------------
       1.new_robust_democracy6 |  -1.118677   .4094854    -2.73   0.006    -1.921254   -.3161008
                        troops |  -.1720984   .3543506    -0.49   0.627    -.8666128    .5224159
                               |
new_robust_democracy6#c.troops |
                            1  |  -.6602458   .7434278    -0.89   0.374    -2.117338     .796846
                               |
                population2000 |  -3.33e-10   6.06e-09    -0.05   0.956    -1.22e-08    1.15e-08
                        gdpcap |   -.007618   .0313899    -0.24   0.808    -.0691412    .0539051
                       durable |  -.0167517    .021227    -0.79   0.430    -.0583558    .0248523
                      conflict |   .8082747   .3447763     2.34   0.019     .1325255    1.484024
                        milexp |  -398.4421   171.2426    -2.33   0.020    -734.0715   -62.81271
                         exsol |   .7746237   2.123611     0.36   0.715    -3.387578    4.936825
                         _cons |  -2.430278    .319332    -7.61   0.000    -3.056157   -1.804399
------------------------------------------------------------------------------------------------
(est15 stored)

. margins, dydx(new_robust_democracy6) post

Average marginal effects                                 Number of obs = 3,001
Model VCE: Robust

Expression: Predicted number of events, predict()
dy/dx wrt:  1.new_robust_democracy6

-----------------------------------------------------------------------------------------
                        |            Delta-method
                        |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
------------------------+----------------------------------------------------------------
1.new_robust_democracy6 |  -.0333935    .009051    -3.69   0.000    -.0511331   -.0156538
-----------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. est store m8a

. 
. 
. *coefplot
. coefplot (m2a, label(Autocracy)) ///
> (m4a, label(Anocracy)) ///
> (m8a, label(New Democracy)), xline(0) ///
> title("", size(medsmall) span) xtitle("Average Marginal Effects", size(small)) ylabel(,labsize(vsmall)) xlabel(,labsize(vsmall)) legend(size(vsmall) span)

. 
end of do-file

. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. *coefplot
. coefplot (m2a, label(Autocracy)) ///
> (m4a, label(Anocracy)) ///
> (m8a, label(New Democracy)), xline(0) ///
> title("", size(medsmall) span) xtitle("Average Marginal Effects", size(small)) ylabel(,labsize(vsmall)) xlabel(,labsize(vsmall)) legend(size(vsmall) span)

. 
. graph save "figures\Study_2\Figure_2_AME_Regime_Type.gph", replace
file figures\Study_2\Figure_2_AME_Regime_Type.gph saved

. 
end of do-file

. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. graph combine "figures\Study_2\Figure_2_original.gph" "figures\Study_2\Figure_2_AME_Regime_Type.gph", col(2)

. 
end of do-file

. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. 
. *coefplot
. coefplot (m2a, label(Autocracy)) ///
> (m4a, label(Anocracy)) ///
> (m8a, label(New Democracy)), xline(0) ///
> title("", size(medsmall) span) xtitle("Average Marginal Effects", size(small)) ylabel(,labsize(vsmall)) xlabel(,labsize(vsmall)) legend(size(vsmall) span) legend(off)

. 
end of do-file

. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. 
. 
. *coefplot
. coefplot (m2a, label(Autocracy)) ///
> (m4a, label(Anocracy)) ///
> (m8a, label(New Democracy)), xline(0) ///
> title("", size(medsmall) span) xtitle("Average Marginal Effects", size(small)) ylabel(,labsize(vsmall)) xlabel(,labsize(vsmall)) legend(size(vsmall) span) legend(off)

. 
. 
. graph save "figures\Study_2\Figure_2_AME_Regime_Type.gph", replace
file figures\Study_2\Figure_2_AME_Regime_Type.gph saved

. 
end of do-file

. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. 
. *coefplot
. coefplot (m1, label(Autocracy * Peacekeepers) lcolor(black) lwidth(thick) lpattern(dash))  (m2, label(Anocracy * Peacekeepers)) (m3, label(Democracy * Peacekeepers)), xline(0) xtitle(Average marginal effects) l
> egend(off)

. 
. graph save "figures\Study_2\Figure_2_original.gph", replace
file figures\Study_2\Figure_2_original.gph saved

. 
end of do-file

. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. 
. graph combine "figures\Study_2\Figure_2_original.gph" "figures\Study_2\Figure_2_AME_Regime_Type.gph", col(2)

. graph save "figures\Study_2\Figure_work_around.gph", replace
file figures\Study_2\Figure_work_around.gph saved

. 
end of do-file

. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. graph combine "figures\Study_2\Figure_work_around.gph" "figures\Study_2\Figure_B3_AME_by_troop_rounded.gph", col(1)

. 
end of do-file

. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

.         
. *model 2a: autocracy
. poisson milcoupsum robust_autocracy6##c.troops population2000 gdpcap durable conflict milexp exsol, cluster(ccode)

Iteration 0:   log pseudolikelihood = -449.22112  
Iteration 1:   log pseudolikelihood = -410.06911  
Iteration 2:   log pseudolikelihood = -404.01836  
Iteration 3:   log pseudolikelihood = -402.11343  
Iteration 4:   log pseudolikelihood = -402.01812  
Iteration 5:   log pseudolikelihood = -402.01764  
Iteration 6:   log pseudolikelihood = -402.01764  

Poisson regression                                      Number of obs =  3,001
                                                        Wald chi2(9)  =  82.80
Log pseudolikelihood = -402.01764                       Prob > chi2   = 0.0000

                                              (Std. err. adjusted for 148 clusters in ccode)
--------------------------------------------------------------------------------------------
                           |               Robust
                milcoupsum | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------------+----------------------------------------------------------------
       1.robust_autocracy6 |   .5278635   .3128468     1.69   0.092    -.0853049    1.141032
                    troops |  -1.838259   1.027145    -1.79   0.074    -3.851427    .1749095
                           |
robust_autocracy6#c.troops |
                        1  |   2.053367   1.009086     2.03   0.042     .0755952    4.031138
                           |
            population2000 |  -2.34e-09   6.43e-09    -0.36   0.716    -1.49e-08    1.03e-08
                    gdpcap |   -.020064   .0477717    -0.42   0.674    -.1136947    .0735667
                   durable |  -.0224258   .0221588    -1.01   0.312    -.0658563    .0210046
                  conflict |   .9358275   .3751772     2.49   0.013     .2004936    1.671161
                    milexp |  -378.6024   181.1369    -2.09   0.037    -733.6242   -23.58055
                     exsol |   .9254581   2.147148     0.43   0.666    -3.282875    5.133792
                     _cons |  -2.714514   .3781303    -7.18   0.000    -3.455636   -1.973392
--------------------------------------------------------------------------------------------

. margins, dydx(robust_autocracy6) at (troops = (0(0.05)9.8)) post

Average marginal effects                                 Number of obs = 3,001
Model VCE: Robust

Expression: Predicted number of events, predict()
dy/dx wrt:  1.robust_autocracy6
1._at:   troops =    0
2._at:   troops =  .05
3._at:   troops =   .1
4._at:   troops =  .15
5._at:   troops =   .2
6._at:   troops =  .25
7._at:   troops =   .3
8._at:   troops =  .35
9._at:   troops =   .4
10._at:  troops =  .45
11._at:  troops =   .5
12._at:  troops =  .55
13._at:  troops =   .6
14._at:  troops =  .65
15._at:  troops =   .7
16._at:  troops =  .75
17._at:  troops =   .8
18._at:  troops =  .85
19._at:  troops =   .9
20._at:  troops =  .95
21._at:  troops =    1
22._at:  troops = 1.05
23._at:  troops =  1.1
24._at:  troops = 1.15
25._at:  troops =  1.2
26._at:  troops = 1.25
27._at:  troops =  1.3
28._at:  troops = 1.35
29._at:  troops =  1.4
30._at:  troops = 1.45
31._at:  troops =  1.5
32._at:  troops = 1.55
33._at:  troops =  1.6
34._at:  troops = 1.65
35._at:  troops =  1.7
36._at:  troops = 1.75
37._at:  troops =  1.8
38._at:  troops = 1.85
39._at:  troops =  1.9
40._at:  troops = 1.95
41._at:  troops =    2
42._at:  troops = 2.05
43._at:  troops =  2.1
44._at:  troops = 2.15
45._at:  troops =  2.2
46._at:  troops = 2.25
47._at:  troops =  2.3
48._at:  troops = 2.35
49._at:  troops =  2.4
50._at:  troops = 2.45
51._at:  troops =  2.5
52._at:  troops = 2.55
53._at:  troops =  2.6
54._at:  troops = 2.65
55._at:  troops =  2.7
56._at:  troops = 2.75
57._at:  troops =  2.8
58._at:  troops = 2.85
59._at:  troops =  2.9
60._at:  troops = 2.95
61._at:  troops =    3
62._at:  troops = 3.05
63._at:  troops =  3.1
64._at:  troops = 3.15
65._at:  troops =  3.2
66._at:  troops = 3.25
67._at:  troops =  3.3
68._at:  troops = 3.35
69._at:  troops =  3.4
70._at:  troops = 3.45
71._at:  troops =  3.5
72._at:  troops = 3.55
73._at:  troops =  3.6
74._at:  troops = 3.65
75._at:  troops =  3.7
76._at:  troops = 3.75
77._at:  troops =  3.8
78._at:  troops = 3.85
79._at:  troops =  3.9
80._at:  troops = 3.95
81._at:  troops =    4
82._at:  troops = 4.05
83._at:  troops =  4.1
84._at:  troops = 4.15
85._at:  troops =  4.2
86._at:  troops = 4.25
87._at:  troops =  4.3
88._at:  troops = 4.35
89._at:  troops =  4.4
90._at:  troops = 4.45
91._at:  troops =  4.5
92._at:  troops = 4.55
93._at:  troops =  4.6
94._at:  troops = 4.65
95._at:  troops =  4.7
96._at:  troops = 4.75
97._at:  troops =  4.8
98._at:  troops = 4.85
99._at:  troops =  4.9
100._at: troops = 4.95
101._at: troops =    5
102._at: troops = 5.05
103._at: troops =  5.1
104._at: troops = 5.15
105._at: troops =  5.2
106._at: troops = 5.25
107._at: troops =  5.3
108._at: troops = 5.35
109._at: troops =  5.4
110._at: troops = 5.45
111._at: troops =  5.5
112._at: troops = 5.55
113._at: troops =  5.6
114._at: troops = 5.65
115._at: troops =  5.7
116._at: troops = 5.75
117._at: troops =  5.8
118._at: troops = 5.85
119._at: troops =  5.9
120._at: troops = 5.95
121._at: troops =    6
122._at: troops = 6.05
123._at: troops =  6.1
124._at: troops = 6.15
125._at: troops =  6.2
126._at: troops = 6.25
127._at: troops =  6.3
128._at: troops = 6.35
129._at: troops =  6.4
130._at: troops = 6.45
131._at: troops =  6.5
132._at: troops = 6.55
133._at: troops =  6.6
134._at: troops = 6.65
135._at: troops =  6.7
136._at: troops = 6.75
137._at: troops =  6.8
138._at: troops = 6.85
139._at: troops =  6.9
140._at: troops = 6.95
141._at: troops =    7
142._at: troops = 7.05
143._at: troops =  7.1
144._at: troops = 7.15
145._at: troops =  7.2
146._at: troops = 7.25
147._at: troops =  7.3
148._at: troops = 7.35
149._at: troops =  7.4
150._at: troops = 7.45
151._at: troops =  7.5
152._at: troops = 7.55
153._at: troops =  7.6
154._at: troops = 7.65
155._at: troops =  7.7
156._at: troops = 7.75
157._at: troops =  7.8
158._at: troops = 7.85
159._at: troops =  7.9
160._at: troops = 7.95
161._at: troops =    8
162._at: troops = 8.05
163._at: troops =  8.1
164._at: troops = 8.15
165._at: troops =  8.2
166._at: troops = 8.25
167._at: troops =  8.3
168._at: troops = 8.35
169._at: troops =  8.4
170._at: troops = 8.45
171._at: troops =  8.5
172._at: troops = 8.55
173._at: troops =  8.6
174._at: troops = 8.65
175._at: troops =  8.7
176._at: troops = 8.75
177._at: troops =  8.8
178._at: troops = 8.85
179._at: troops =  8.9
180._at: troops = 8.95
181._at: troops =    9
182._at: troops = 9.05
183._at: troops =  9.1
184._at: troops = 9.15
185._at: troops =  9.2
186._at: troops = 9.25
187._at: troops =  9.3
188._at: troops = 9.35
189._at: troops =  9.4
190._at: troops = 9.45
191._at: troops =  9.5
192._at: troops = 9.55
193._at: troops =  9.6
194._at: troops = 9.65
195._at: troops =  9.7
196._at: troops = 9.75
197._at: troops =  9.8

--------------------------------------------------------------------------------------
                     |            Delta-method
                     |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
---------------------+----------------------------------------------------------------
0.robust_autocracy6  |  (base outcome)
---------------------+----------------------------------------------------------------
1.robust_autocracy6  |
                 _at |
                  1  |   .0240775   .0166719     1.44   0.149    -.0085989    .0567539
                  2  |   .0277532   .0164575     1.69   0.092    -.0045028    .0600093
                  3  |   .0311688   .0164246     1.90   0.058    -.0010229    .0633605
                  4  |   .0343477   .0164992     2.08   0.037       .00201    .0666855
                  5  |   .0373115    .016629     2.24   0.025     .0047192    .0699037
                  6  |   .0400796   .0167799     2.39   0.017     .0071917    .0729675
                  7  |     .04267   .0169309     2.52   0.012      .009486     .075854
                  8  |    .045099   .0170705     2.64   0.008     .0116414    .0785566
                  9  |   .0473815   .0171934     2.76   0.006     .0136831    .0810799
                 10  |    .049531   .0172982     2.86   0.004     .0156271     .083435
                 11  |   .0515601   .0173863     2.97   0.003     .0174835    .0856366
                 12  |   .0534799   .0174603     3.06   0.002     .0192584    .0877014
                 13  |   .0553008   .0175235     3.16   0.002     .0209553    .0896464
                 14  |   .0570324   .0175799     3.24   0.001     .0225765    .0914883
                 15  |   .0586831   .0176329     3.33   0.001     .0241232     .093243
                 16  |   .0602608   .0176862     3.41   0.001     .0255965    .0949252
                 17  |   .0617728   .0177429     3.48   0.000     .0269973    .0965483
                 18  |   .0632256   .0178059     3.55   0.000     .0283266    .0981245
                 19  |   .0646252   .0178776     3.61   0.000     .0295857    .0996646
                 20  |    .065977     .01796     3.67   0.000      .030776     .101178
                 21  |   .0672861   .0180549     3.73   0.000     .0318992    .1026731
                 22  |   .0685571   .0181636     3.77   0.000     .0329571    .1041571
                 23  |   .0697941   .0182872     3.82   0.000     .0339518    .1056364
                 24  |   .0710009   .0184265     3.85   0.000     .0348856    .1071162
                 25  |   .0721811   .0185821     3.88   0.000     .0357608    .1086014
                 26  |   .0733378   .0187544     3.91   0.000     .0365798    .1100958
                 27  |    .074474   .0189436     3.93   0.000     .0373452    .1116029
                 28  |   .0755924   .0191499     3.95   0.000     .0380592    .1131256
                 29  |   .0766953   .0193733     3.96   0.000     .0387244    .1146663
                 30  |   .0777852   .0196137     3.97   0.000      .039343    .1162273
                 31  |   .0788639    .019871     3.97   0.000     .0399173    .1178104
                 32  |   .0799334   .0201452     3.97   0.000     .0404495    .1194173
                 33  |   .0809955   .0204361     3.96   0.000     .0409415    .1210494
                 34  |   .0820516   .0207434     3.96   0.000     .0413953     .122708
                 35  |   .0831034   .0210671     3.94   0.000     .0418126    .1243942
                 36  |   .0841521   .0214071     3.93   0.000      .042195    .1261091
                 37  |   .0851989   .0217631     3.91   0.000     .0425441    .1278538
                 38  |   .0862451   .0221351     3.90   0.000     .0428611     .129629
                 39  |   .0872916    .022523     3.88   0.000     .0431474    .1314358
                 40  |   .0883394   .0229266     3.85   0.000      .043404    .1332748
                 41  |   .0893894   .0233461     3.83   0.000      .043632    .1351469
                 42  |   .0904425   .0237812     3.80   0.000     .0438322    .1370528
                 43  |   .0914993   .0242321     3.78   0.000     .0440053    .1389933
                 44  |   .0925607   .0246986     3.75   0.000     .0441522    .1409691
                 45  |   .0936272   .0251809     3.72   0.000     .0442734    .1429809
                 46  |   .0946995    .025679     3.69   0.000     .0443695    .1450295
                 47  |   .0957781    .026193     3.66   0.000     .0444408    .1471154
                 48  |   .0968636   .0267228     3.62   0.000     .0444879    .1492393
                 49  |   .0979565   .0272687     3.59   0.000     .0445109     .151402
                 50  |   .0990571   .0278306     3.56   0.000     .0445102    .1536041
                 51  |   .1001661   .0284088     3.53   0.000      .044486    .1558462
                 52  |   .1012837   .0290032     3.49   0.000     .0444384     .158129
                 53  |   .1024104   .0296142     3.46   0.001     .0443677    .1604531
                 54  |   .1035465   .0302417     3.42   0.001     .0442738    .1628192
                 55  |   .1046924    .030886     3.39   0.001     .0441569    .1652278
                 56  |   .1058483   .0315472     3.36   0.001     .0440169    .1676797
                 57  |   .1070147   .0322255     3.32   0.001     .0438539    .1701754
                 58  |   .1081917   .0329209     3.29   0.001     .0436679    .1727156
                 59  |   .1093798   .0336338     3.25   0.001     .0434587    .1753008
                 60  |   .1105791   .0343643     3.22   0.001     .0432264    .1779318
                 61  |   .1117899   .0351125     3.18   0.001     .0429707    .1806091
                 62  |   .1130125   .0358786     3.15   0.002     .0426917    .1833333
                 63  |   .1142471   .0366629     3.12   0.002     .0423891     .186105
                 64  |   .1154939   .0374655     3.08   0.002     .0420629     .188925
                 65  |   .1167533   .0382867     3.05   0.002     .0417128    .1917938
                 66  |   .1180253   .0391266     3.02   0.003     .0413386    .1947119
                 67  |   .1193102   .0399854     2.98   0.003     .0409403    .1976802
                 68  |   .1206083   .0408634     2.95   0.003     .0405175    .2006991
                 69  |   .1219197   .0417608     2.92   0.004     .0400701    .2037693
                 70  |   .1232447   .0426777     2.89   0.004     .0395979    .2068915
                 71  |   .1245834   .0436145     2.86   0.004     .0391005    .2100663
                 72  |   .1259361   .0445714     2.83   0.005     .0385778    .2132944
                 73  |   .1273029   .0455485     2.79   0.005     .0380294    .2165763
                 74  |    .128684   .0465462     2.76   0.006     .0374551    .2199129
                 75  |   .1300797   .0475647     2.73   0.006     .0368547    .2233047
                 76  |   .1314901   .0486041     2.71   0.007     .0362278    .2267524
                 77  |   .1329154   .0496649     2.68   0.007     .0355741    .2302568
                 78  |   .1343559   .0507471     2.65   0.008     .0348933    .2338184
                 79  |   .1358116   .0518512     2.62   0.009     .0341851    .2374381
                 80  |   .1372828   .0529773     2.59   0.010     .0334492    .2411165
                 81  |   .1387697   .0541258     2.56   0.010     .0326852    .2448543
                 82  |   .1402725   .0552968     2.54   0.011     .0318927    .2486523
                 83  |   .1417913   .0564908     2.51   0.012     .0310714    .2525112
                 84  |   .1433264   .0577079     2.48   0.013      .030221    .2564318
                 85  |   .1448779   .0589485     2.46   0.014      .029341    .2604147
                 86  |    .146446   .0602128     2.43   0.015     .0284311    .2644609
                 87  |    .148031   .0615012     2.41   0.016     .0274909    .2685711
                 88  |    .149633   .0628139     2.38   0.017     .0265199     .272746
                 89  |   .1512522   .0641514     2.36   0.018     .0255178    .2769866
                 90  |   .1528888   .0655138     2.33   0.020     .0244841    .2812935
                 91  |    .154543   .0669015     2.31   0.021     .0234184    .2856676
                 92  |    .156215   .0683149     2.29   0.022     .0223203    .2901098
                 93  |    .157905   .0697543     2.26   0.024     .0211892    .2946209
                 94  |   .1596133   .0712199     2.24   0.025     .0200248    .2992018
                 95  |   .1613399   .0727123     2.22   0.026     .0188265    .3038533
                 96  |   .1630851   .0742316     2.20   0.028     .0175939    .3085764
                 97  |   .1648492   .0757783     2.18   0.030     .0163265    .3133719
                 98  |   .1666323   .0773527     2.15   0.031     .0150238    .3182408
                 99  |   .1684346   .0789552     2.13   0.033     .0136852     .323184
                100  |   .1702564   .0805862     2.11   0.035     .0123104    .3282024
                101  |   .1720978    .082246     2.09   0.036     .0108986    .3332971
                102  |   .1739592   .0839351     2.07   0.038     .0094494    .3384689
                103  |   .1758406   .0856537     2.05   0.040     .0079623    .3437188
                104  |   .1777423   .0874024     2.03   0.042     .0064367    .3490479
                105  |   .1796646   .0891815     2.01   0.044      .004872    .3544572
                106  |   .1816076   .0909915     2.00   0.046     .0032676    .3599477
                107  |   .1835716   .0928327     1.98   0.048     .0016229    .3655204
                108  |   .1855569   .0947055     1.96   0.050    -.0000626    .3711763
                109  |   .1875636   .0966105     1.94   0.052    -.0017896    .3769167
                110  |   .1895919    .098548     1.92   0.054    -.0035586    .3827425
                111  |   .1916422   .1005185     1.91   0.057    -.0053704    .3886549
                112  |   .1937147   .1025224     1.89   0.059    -.0072256     .394655
                113  |   .1958095   .1045602     1.87   0.061    -.0091247    .4007438
                114  |    .197927   .1066324     1.86   0.063    -.0110686    .4069226
                115  |   .2000674   .1087393     1.84   0.066    -.0130578    .4131926
                116  |   .2022309   .1108816     1.82   0.068     -.015093    .4195549
                117  |   .2044178   .1130596     1.81   0.071     -.017175    .4260107
                118  |   .2066284   .1152739     1.79   0.073    -.0193044    .4325611
                119  |   .2088628    .117525     1.78   0.076     -.021482    .4392076
                120  |   .2111214   .1198134     1.76   0.078    -.0237085    .4459513
                121  |   .2134044   .1221395     1.75   0.081    -.0259847    .4527935
                122  |   .2157121    .124504     1.73   0.083    -.0283112    .4597354
                123  |   .2180447   .1269073     1.72   0.086    -.0306889    .4667784
                124  |   .2204026   .1293499     1.70   0.088    -.0331186    .4739238
                125  |   .2227859   .1318325     1.69   0.091     -.035601    .4811729
                126  |   .2251951   .1343555     1.68   0.094    -.0381369    .4885271
                127  |   .2276303   .1369196     1.66   0.096    -.0407273    .4959878
                128  |   .2300917   .1395253     1.65   0.099    -.0433728    .5035562
                129  |   .2325799   .1421731     1.64   0.102    -.0460743     .511234
                130  |   .2350949   .1448637     1.62   0.105    -.0488327    .5190224
                131  |   .2376371   .1475976     1.61   0.107    -.0516489    .5269231
                132  |   .2402068   .1503755     1.60   0.110    -.0545237    .5349373
                133  |   .2428043   .1531979     1.58   0.113    -.0574581    .5430666
                134  |   .2454298   .1560655     1.57   0.116    -.0604529    .5513125
                135  |   .2480838   .1589788     1.56   0.119     -.063509    .5596766
                136  |   .2507665   .1619386     1.55   0.121    -.0666275    .5681604
                137  |   .2534781   .1649455     1.54   0.124    -.0698092    .5767654
                138  |   .2562191   .1680001     1.53   0.127    -.0730551    .5854933
                139  |   .2589897   .1711031     1.51   0.130    -.0763662    .5943457
                140  |   .2617903   .1742551     1.50   0.133    -.0797435    .6033241
                141  |   .2646212   .1774569     1.49   0.136     -.083188    .6124303
                142  |   .2674827   .1807091     1.48   0.139    -.0867007     .621666
                143  |   .2703751   .1840124     1.47   0.142    -.0902827    .6310328
                144  |   .2732988   .1873676     1.46   0.145     -.093935    .6405325
                145  |   .2762541   .1907753     1.45   0.148    -.0976586    .6501667
                146  |   .2792413   .1942362     1.44   0.151    -.1014547    .6599374
                147  |   .2822609   .1977512     1.43   0.153    -.1053244    .6698462
                148  |   .2853131    .201321     1.42   0.156    -.1092688     .679895
                149  |   .2883983   .2049463     1.41   0.159    -.1132889    .6900856
                150  |   .2915169   .2086278     1.40   0.162     -.117386    .7004198
                151  |   .2946692   .2123664     1.39   0.165    -.1215613    .7108997
                152  |   .2978556   .2161629     1.38   0.168    -.1258159    .7215271
                153  |   .3010765    .220018     1.37   0.171    -.1301509    .7323038
                154  |   .3043321   .2239326     1.36   0.174    -.1345677     .743232
                155  |    .307623   .2279075     1.35   0.177    -.1390674    .7543135
                156  |   .3109495   .2319435     1.34   0.180    -.1436514    .7655504
                157  |   .3143119   .2360415     1.33   0.183    -.1483209    .7769447
                158  |   .3177107   .2402022     1.32   0.186     -.153077    .7884985
                159  |   .3211463   .2444267     1.31   0.189    -.1579213    .8002139
                160  |    .324619   .2487158     1.31   0.192     -.162855    .8120929
                161  |   .3281292   .2530703     1.30   0.195    -.1678794    .8241378
                162  |   .3316774   .2574912     1.29   0.198    -.1729959    .8363508
                163  |    .335264   .2619793     1.28   0.201     -.178206     .848734
                164  |   .3388893   .2665356     1.27   0.204    -.1835108    .8612895
                165  |   .3425539   .2711611     1.26   0.206     -.188912    .8740198
                166  |   .3462581   .2758567     1.26   0.209     -.194411    .8869272
                167  |   .3500024   .2806233     1.25   0.212    -.2000092     .900014
                168  |   .3537871    .285462     1.24   0.215    -.2057081    .9132824
                169  |   .3576127   .2903736     1.23   0.218    -.2115091    .9267345
                170  |   .3614798   .2953594     1.22   0.221    -.2174139    .9403735
                171  |   .3653886   .3004201     1.22   0.224     -.223424    .9542013
                172  |   .3693397    .305557     1.21   0.227     -.229541    .9682205
                173  |   .3733336    .310771     1.20   0.230    -.2357664    .9824336
                174  |   .3773706   .3160631     1.19   0.232    -.2421017    .9968428
                175  |   .3814513   .3214345     1.19   0.235    -.2485488    1.011451
                176  |   .3855761   .3268863     1.18   0.238    -.2551092    1.026261
                177  |   .3897455   .3324195     1.17   0.241    -.2617847    1.041276
                178  |     .39396   .3380352     1.17   0.244    -.2685769    1.056497
                179  |     .39822   .3437346     1.16   0.247    -.2754874    1.071927
                180  |   .4025261   .3495189     1.15   0.249    -.2825182    1.087571
                181  |   .4068789   .3553891     1.14   0.252     -.289671    1.103429
                182  |   .4112786   .3613466     1.14   0.255    -.2969476    1.119505
                183  |    .415726   .3673924     1.13   0.258    -.3043498    1.135802
                184  |   .4202214   .3735276     1.13   0.261    -.3118794    1.152322
                185  |   .4247654   .3797538     1.12   0.263    -.3195384    1.169069
                186  |   .4293586    .386072     1.11   0.266    -.3273286    1.186046
                187  |   .4340015   .3924835     1.11   0.269     -.335252    1.203255
                188  |   .4386945   .3989895     1.10   0.272    -.3433105      1.2207
                189  |   .4434383   .4055913     1.09   0.274     -.351506    1.238383
                190  |   .4482334   .4122903     1.09   0.277    -.3598408    1.256308
                191  |   .4530803   .4190878     1.08   0.280    -.3683167    1.274477
                192  |   .4579797   .4259851     1.08   0.282    -.3769358    1.292895
                193  |   .4629321   .4329836     1.07   0.285    -.3857002    1.311564
                194  |   .4679379   .4400845     1.06   0.288    -.3946119    1.330488
                195  |   .4729979   .4472895     1.06   0.290    -.4036734    1.349669
                196  |   .4781127   .4545999     1.05   0.293    -.4128867    1.369112
                197  |   .4832828    .462017     1.05   0.296    -.4222539    1.388819
--------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. marginsplot, ///
>         recast(line) plot1opts(lcolor(gs8)) ciopt(color(black%20)) recastci(rarea) ///
>         ytitle("AME", size(medsmall)) ///
>         xtitle("Troops", size(medsmall))  ///
>         title("Autocracy", size(medium)) yline(0, lcolor(red)) ///
>         addplot(hist troops, /// adding histogram marginsplot graph
> blcolor(black%30) fcolor(white%10) /// bar line and fill colors
> percent /// histogram bins in "percent" rather than "discrete" (actual)
> yaxis(2) /// calls for 2nd y-axis
> yscale(alt lcolor() axis(2)) /// scaling on 2nd y-axis
> ylabel(0 "0%" 20 "20%" 40 "40%" 60 "60%" 80 "80%" 100 "100%", /// labels on 2nd y-axis
> labcolor() axis(2) tlcolor(black) tlwidth(thin) labsize(small)) /// label options on 2nd y-axis
> ytitle(" ", axis(2)))  legend(off) title("C")

Variables that uniquely identify margins: troops

. graph save "figures\Study_2\B2.2.gph", replace
file figures\Study_2\B2.2.gph saved

. 
. 
. *model 4a: anocracy
. poisson milcoupsum robust_anocracy5##c.troops population2000 gdpcap durable conflict milexp exsol, cluster(ccode)

Iteration 0:   log pseudolikelihood = -418.40873  
Iteration 1:   log pseudolikelihood =  -412.0033  
Iteration 2:   log pseudolikelihood =   -407.598  
Iteration 3:   log pseudolikelihood = -405.16414  
Iteration 4:   log pseudolikelihood = -405.09412  
Iteration 5:   log pseudolikelihood = -405.09351  
Iteration 6:   log pseudolikelihood = -405.09351  

Poisson regression                                      Number of obs =  3,001
                                                        Wald chi2(9)  =  47.18
Log pseudolikelihood = -405.09351                       Prob > chi2   = 0.0000

                                             (Std. err. adjusted for 148 clusters in ccode)
-------------------------------------------------------------------------------------------
                          |               Robust
               milcoupsum | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------------------+----------------------------------------------------------------
       1.robust_anocracy5 |   .6782844   .2786318     2.43   0.015     .1321761    1.224393
                   troops |   .0150535   .2278477     0.07   0.947    -.4315197    .4616267
                          |
robust_anocracy5#c.troops |
                       1  |  -2.774543   .7840732    -3.54   0.000    -4.311299   -1.237788
                          |
           population2000 |  -4.52e-10   5.94e-09    -0.08   0.939    -1.21e-08    1.12e-08
                   gdpcap |  -.0128145   .0410951    -0.31   0.755    -.0933595    .0677304
                  durable |  -.0147644     .02168    -0.68   0.496    -.0572563    .0277276
                 conflict |   .9400657   .3531598     2.66   0.008     .2478853    1.632246
                   milexp |  -426.8629   172.5747    -2.47   0.013    -765.1032   -88.62272
                    exsol |   1.080705   2.444078     0.44   0.658    -3.709601     5.87101
                    _cons |  -3.068939   .3479924    -8.82   0.000    -3.750991   -2.386886
-------------------------------------------------------------------------------------------

. margins, dydx(robust_anocracy5) at (troops =  (0(0.05)9.8)) post

Average marginal effects                                 Number of obs = 3,001
Model VCE: Robust

Expression: Predicted number of events, predict()
dy/dx wrt:  1.robust_anocracy5
1._at:   troops =    0
2._at:   troops =  .05
3._at:   troops =   .1
4._at:   troops =  .15
5._at:   troops =   .2
6._at:   troops =  .25
7._at:   troops =   .3
8._at:   troops =  .35
9._at:   troops =   .4
10._at:  troops =  .45
11._at:  troops =   .5
12._at:  troops =  .55
13._at:  troops =   .6
14._at:  troops =  .65
15._at:  troops =   .7
16._at:  troops =  .75
17._at:  troops =   .8
18._at:  troops =  .85
19._at:  troops =   .9
20._at:  troops =  .95
21._at:  troops =    1
22._at:  troops = 1.05
23._at:  troops =  1.1
24._at:  troops = 1.15
25._at:  troops =  1.2
26._at:  troops = 1.25
27._at:  troops =  1.3
28._at:  troops = 1.35
29._at:  troops =  1.4
30._at:  troops = 1.45
31._at:  troops =  1.5
32._at:  troops = 1.55
33._at:  troops =  1.6
34._at:  troops = 1.65
35._at:  troops =  1.7
36._at:  troops = 1.75
37._at:  troops =  1.8
38._at:  troops = 1.85
39._at:  troops =  1.9
40._at:  troops = 1.95
41._at:  troops =    2
42._at:  troops = 2.05
43._at:  troops =  2.1
44._at:  troops = 2.15
45._at:  troops =  2.2
46._at:  troops = 2.25
47._at:  troops =  2.3
48._at:  troops = 2.35
49._at:  troops =  2.4
50._at:  troops = 2.45
51._at:  troops =  2.5
52._at:  troops = 2.55
53._at:  troops =  2.6
54._at:  troops = 2.65
55._at:  troops =  2.7
56._at:  troops = 2.75
57._at:  troops =  2.8
58._at:  troops = 2.85
59._at:  troops =  2.9
60._at:  troops = 2.95
61._at:  troops =    3
62._at:  troops = 3.05
63._at:  troops =  3.1
64._at:  troops = 3.15
65._at:  troops =  3.2
66._at:  troops = 3.25
67._at:  troops =  3.3
68._at:  troops = 3.35
69._at:  troops =  3.4
70._at:  troops = 3.45
71._at:  troops =  3.5
72._at:  troops = 3.55
73._at:  troops =  3.6
74._at:  troops = 3.65
75._at:  troops =  3.7
76._at:  troops = 3.75
77._at:  troops =  3.8
78._at:  troops = 3.85
79._at:  troops =  3.9
80._at:  troops = 3.95
81._at:  troops =    4
82._at:  troops = 4.05
83._at:  troops =  4.1
84._at:  troops = 4.15
85._at:  troops =  4.2
86._at:  troops = 4.25
87._at:  troops =  4.3
88._at:  troops = 4.35
89._at:  troops =  4.4
90._at:  troops = 4.45
91._at:  troops =  4.5
92._at:  troops = 4.55
93._at:  troops =  4.6
94._at:  troops = 4.65
95._at:  troops =  4.7
96._at:  troops = 4.75
97._at:  troops =  4.8
98._at:  troops = 4.85
99._at:  troops =  4.9
100._at: troops = 4.95
101._at: troops =    5
102._at: troops = 5.05
103._at: troops =  5.1
104._at: troops = 5.15
105._at: troops =  5.2
106._at: troops = 5.25
107._at: troops =  5.3
108._at: troops = 5.35
109._at: troops =  5.4
110._at: troops = 5.45
111._at: troops =  5.5
112._at: troops = 5.55
113._at: troops =  5.6
114._at: troops = 5.65
115._at: troops =  5.7
116._at: troops = 5.75
117._at: troops =  5.8
118._at: troops = 5.85
119._at: troops =  5.9
120._at: troops = 5.95
121._at: troops =    6
122._at: troops = 6.05
123._at: troops =  6.1
124._at: troops = 6.15
125._at: troops =  6.2
126._at: troops = 6.25
127._at: troops =  6.3
128._at: troops = 6.35
129._at: troops =  6.4
130._at: troops = 6.45
131._at: troops =  6.5
132._at: troops = 6.55
133._at: troops =  6.6
134._at: troops = 6.65
135._at: troops =  6.7
136._at: troops = 6.75
137._at: troops =  6.8
138._at: troops = 6.85
139._at: troops =  6.9
140._at: troops = 6.95
141._at: troops =    7
142._at: troops = 7.05
143._at: troops =  7.1
144._at: troops = 7.15
145._at: troops =  7.2
146._at: troops = 7.25
147._at: troops =  7.3
148._at: troops = 7.35
149._at: troops =  7.4
150._at: troops = 7.45
151._at: troops =  7.5
152._at: troops = 7.55
153._at: troops =  7.6
154._at: troops = 7.65
155._at: troops =  7.7
156._at: troops = 7.75
157._at: troops =  7.8
158._at: troops = 7.85
159._at: troops =  7.9
160._at: troops = 7.95
161._at: troops =    8
162._at: troops = 8.05
163._at: troops =  8.1
164._at: troops = 8.15
165._at: troops =  8.2
166._at: troops = 8.25
167._at: troops =  8.3
168._at: troops = 8.35
169._at: troops =  8.4
170._at: troops = 8.45
171._at: troops =  8.5
172._at: troops = 8.55
173._at: troops =  8.6
174._at: troops = 8.65
175._at: troops =  8.7
176._at: troops = 8.75
177._at: troops =  8.8
178._at: troops = 8.85
179._at: troops =  8.9
180._at: troops = 8.95
181._at: troops =    9
182._at: troops = 9.05
183._at: troops =  9.1
184._at: troops = 9.15
185._at: troops =  9.2
186._at: troops = 9.25
187._at: troops =  9.3
188._at: troops = 9.35
189._at: troops =  9.4
190._at: troops = 9.45
191._at: troops =  9.5
192._at: troops = 9.55
193._at: troops =  9.6
194._at: troops = 9.65
195._at: troops =  9.7
196._at: troops = 9.75
197._at: troops =  9.8

-------------------------------------------------------------------------------------
                    |            Delta-method
                    |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
--------------------+----------------------------------------------------------------
0.robust_anocracy5  |  (base outcome)
--------------------+----------------------------------------------------------------
1.robust_anocracy5  |
                _at |
                 1  |   .0262459   .0113033     2.32   0.020     .0040918    .0483999
                 2  |   .0193576   .0099818     1.94   0.052    -.0002065    .0389216
                 3  |   .0133544   .0090922     1.47   0.142     -.004466    .0311748
                 4  |   .0081223   .0084958     0.96   0.339    -.0085292    .0247737
                 5  |   .0035618   .0080846     0.44   0.660    -.0122837    .0194074
                 6  |  -.0004135   .0077838    -0.05   0.958    -.0156696    .0148425
                 7  |  -.0038792   .0075471    -0.51   0.607    -.0186712    .0109128
                 8  |  -.0069009   .0073487    -0.94   0.348    -.0213041    .0075024
                 9  |  -.0095358   .0071768    -1.33   0.184     -.023602    .0045305
                10  |  -.0118337   .0070274    -1.68   0.092    -.0256072    .0019398
                11  |  -.0138382   .0069011    -2.01   0.045     -.027364   -.0003123
                12  |  -.0155869   .0068001    -2.29   0.022    -.0289148   -.0022591
                13  |   -.017113   .0067271    -2.54   0.011    -.0302978   -.0039282
                14  |   -.018445   .0066843    -2.76   0.006    -.0315461    -.005344
                15  |  -.0196081   .0066731    -2.94   0.003    -.0326872    -.006529
                16  |  -.0206239   .0066936    -3.08   0.002    -.0337431   -.0075047
                17  |  -.0215114    .006745    -3.19   0.001    -.0347314   -.0082915
                18  |  -.0222873   .0068256    -3.27   0.001    -.0356652   -.0089094
                19  |  -.0229658   .0069332    -3.31   0.001    -.0365547    -.009377
                20  |  -.0235596   .0070652    -3.33   0.001    -.0374072   -.0097119
                21  |  -.0240795    .007219    -3.34   0.001    -.0382284   -.0099306
                22  |   -.024535   .0073917    -3.32   0.001    -.0390224   -.0100476
                23  |  -.0249346   .0075808    -3.29   0.001    -.0397928   -.0100764
                24  |  -.0252853   .0077842    -3.25   0.001     -.040542   -.0100286
                25  |  -.0255935   .0079996    -3.20   0.001    -.0412725   -.0099146
                26  |  -.0258647   .0082254    -3.14   0.002    -.0419862   -.0097432
                27  |  -.0261036     .00846    -3.09   0.002    -.0426849   -.0095223
                28  |  -.0263144   .0087022    -3.02   0.002    -.0433704   -.0092584
                29  |  -.0265008   .0089509    -2.96   0.003    -.0440442   -.0089573
                30  |  -.0266658   .0092052    -2.90   0.004    -.0447077   -.0086239
                31  |  -.0268122   .0094644    -2.83   0.005    -.0453622   -.0082623
                32  |  -.0269425    .009728    -2.77   0.006     -.046009    -.007876
                33  |  -.0270587   .0099954    -2.71   0.007    -.0466493    -.007468
                34  |  -.0271626   .0102663    -2.65   0.008    -.0472841   -.0070411
                35  |  -.0272558   .0105402    -2.59   0.010    -.0479143   -.0065973
                36  |  -.0273397   .0108171    -2.53   0.011    -.0485408   -.0061387
                37  |  -.0274155   .0110965    -2.47   0.013    -.0491643   -.0056667
                38  |  -.0274843   .0113785    -2.42   0.016    -.0497857   -.0051829
                39  |  -.0275469   .0116627    -2.36   0.018    -.0504053   -.0046884
                40  |  -.0276041   .0119491    -2.31   0.021    -.0510239   -.0041843
                41  |  -.0276567   .0122376    -2.26   0.024    -.0516419   -.0036715
                42  |  -.0277052    .012528    -2.21   0.027    -.0522597   -.0031508
                43  |  -.0277502   .0128203    -2.16   0.030    -.0528776   -.0026229
                44  |  -.0277921   .0131144    -2.12   0.034    -.0534959   -.0020883
                45  |  -.0278314   .0134103    -2.08   0.038     -.054115   -.0015477
                46  |  -.0278683   .0137078    -2.03   0.042     -.054735   -.0010015
                47  |  -.0279032   .0140069    -1.99   0.046    -.0553562   -.0004502
                48  |  -.0279363   .0143076    -1.95   0.051    -.0559786    .0001061
                49  |  -.0279679   .0146098    -1.91   0.056    -.0566025    .0006668
                50  |  -.0279981   .0149134    -1.88   0.060    -.0572279    .0012317
                51  |  -.0280272   .0152185    -1.84   0.066    -.0578549    .0018005
                52  |  -.0280553   .0155249    -1.81   0.071    -.0584836    .0023731
                53  |  -.0280824   .0158327    -1.77   0.076     -.059114    .0029491
                54  |  -.0281089   .0161418    -1.74   0.082    -.0597463    .0035285
                55  |  -.0281347   .0164522    -1.71   0.087    -.0603804     .004111
                56  |  -.0281599   .0167638    -1.68   0.093    -.0610163    .0046966
                57  |  -.0281846   .0170766    -1.65   0.099     -.061654    .0052849
                58  |  -.0282088   .0173905    -1.62   0.105    -.0622937     .005876
                59  |  -.0282327   .0177057    -1.59   0.111    -.0629352    .0064697
                60  |  -.0282563   .0180219    -1.57   0.117    -.0635785     .007066
                61  |  -.0282796   .0183392    -1.54   0.123    -.0642237    .0076646
                62  |  -.0283026   .0186576    -1.52   0.129    -.0648708    .0082655
                63  |  -.0283255    .018977    -1.49   0.136    -.0655197    .0088688
                64  |  -.0283481   .0192974    -1.47   0.142    -.0661704    .0094741
                65  |  -.0283706   .0196189    -1.45   0.148    -.0668229    .0100816
                66  |   -.028393   .0199413    -1.42   0.154    -.0674772    .0106912
                67  |  -.0284153   .0202646    -1.40   0.161    -.0681332    .0113027
                68  |  -.0284374   .0205889    -1.38   0.167     -.068791    .0119161
                69  |  -.0284595   .0209142    -1.36   0.174    -.0694505    .0125315
                70  |  -.0284815   .0212403    -1.34   0.180    -.0701117    .0131487
                71  |  -.0285035   .0215673    -1.32   0.186    -.0707746    .0137677
                72  |  -.0285254   .0218952    -1.30   0.193    -.0714391    .0143884
                73  |  -.0285472   .0222239    -1.28   0.199    -.0721053    .0150109
                74  |  -.0285691   .0225535    -1.27   0.205    -.0727731     .015635
                75  |  -.0285909   .0228839    -1.25   0.212    -.0734426    .0162608
                76  |  -.0286126   .0232152    -1.23   0.218    -.0741136    .0168883
                77  |  -.0286344   .0235472    -1.22   0.224    -.0747862    .0175173
                78  |  -.0286562   .0238801    -1.20   0.230    -.0754603     .018148
                79  |  -.0286779   .0242137    -1.18   0.236    -.0761359    .0187801
                80  |  -.0286996   .0245481    -1.17   0.242    -.0768131    .0194138
                81  |  -.0287214   .0248833    -1.15   0.248    -.0774918     .020049
                82  |  -.0287431   .0252193    -1.14   0.254     -.078172    .0206857
                83  |  -.0287649    .025556    -1.13   0.260    -.0788536    .0213239
                84  |  -.0287866   .0258934    -1.11   0.266    -.0795367    .0219635
                85  |  -.0288084   .0262315    -1.10   0.272    -.0802212    .0226045
                86  |  -.0288301   .0265704    -1.09   0.278    -.0809072     .023247
                87  |  -.0288519   .0269101    -1.07   0.284    -.0815946    .0238909
                88  |  -.0288736   .0272504    -1.06   0.289    -.0822834    .0245361
                89  |  -.0288954   .0275914    -1.05   0.295    -.0829736    .0251827
                90  |  -.0289172   .0279331    -1.04   0.301    -.0836651    .0258307
                91  |   -.028939   .0282755    -1.02   0.306    -.0843581      .02648
                92  |  -.0289608   .0286187    -1.01   0.312    -.0850524    .0271307
                93  |  -.0289827   .0289624    -1.00   0.317     -.085748    .0277827
                94  |  -.0290045   .0293069    -0.99   0.322     -.086445     .028436
                95  |  -.0290264   .0296521    -0.98   0.328    -.0871434    .0290906
                96  |  -.0290482   .0299979    -0.97   0.333     -.087843    .0297465
                97  |  -.0290701   .0303444    -0.96   0.338     -.088544    .0304037
                98  |   -.029092   .0306915    -0.95   0.343    -.0892463    .0310622
                99  |  -.0291139   .0310393    -0.94   0.348    -.0899498    .0317219
               100  |  -.0291359   .0313877    -0.93   0.353    -.0906547     .032383
               101  |  -.0291578   .0317368    -0.92   0.358    -.0913609    .0330452
               102  |  -.0291798   .0320866    -0.91   0.363    -.0920683    .0337088
               103  |  -.0292018    .032437    -0.90   0.368    -.0927771    .0343735
               104  |  -.0292238    .032788    -0.89   0.373     -.093487    .0350395
               105  |  -.0292458   .0331397    -0.88   0.378    -.0941983    .0357068
               106  |  -.0292678   .0334919    -0.87   0.382    -.0949108    .0363752
               107  |  -.0292898   .0338449    -0.87   0.387    -.0956246    .0370449
               108  |  -.0293119   .0341984    -0.86   0.391    -.0963396    .0377158
               109  |   -.029334   .0345526    -0.85   0.396    -.0970558    .0383879
               110  |  -.0293561   .0349074    -0.84   0.400    -.0977733    .0390612
               111  |  -.0293782   .0352628    -0.83   0.405     -.098492    .0397357
               112  |  -.0294003   .0356189    -0.83   0.409     -.099212    .0404114
               113  |  -.0294224   .0359755    -0.82   0.413    -.0999332    .0410883
               114  |  -.0294446   .0363328    -0.81   0.418    -.1006556    .0417664
               115  |  -.0294667   .0366907    -0.80   0.422    -.1013792    .0424457
               116  |  -.0294889   .0370492    -0.80   0.426     -.102104    .0431261
               117  |  -.0295111   .0374083    -0.79   0.430    -.1028301    .0438078
               118  |  -.0295334    .037768    -0.78   0.434    -.1035573    .0444906
               119  |  -.0295556   .0381283    -0.78   0.438    -.1042858    .0451746
               120  |  -.0295779   .0384893    -0.77   0.442    -.1050154    .0458597
               121  |  -.0296001   .0388508    -0.76   0.446    -.1057463     .046546
               122  |  -.0296224   .0392129    -0.76   0.450    -.1064784    .0472335
               123  |  -.0296447   .0395757    -0.75   0.454    -.1072116    .0479222
               124  |   -.029667    .039939    -0.74   0.458     -.107946     .048612
               125  |  -.0296894   .0403029    -0.74   0.461    -.1086817    .0493029
               126  |  -.0297117   .0406675    -0.73   0.465    -.1094185     .049995
               127  |  -.0297341   .0410326    -0.72   0.469    -.1101565    .0506883
               128  |  -.0297565   .0413983    -0.72   0.472    -.1108957    .0513827
               129  |  -.0297789   .0417646    -0.71   0.476    -.1116361    .0520783
               130  |  -.0298013   .0421316    -0.71   0.479    -.1123777     .052775
               131  |  -.0298238   .0424991    -0.70   0.483    -.1131204    .0534729
               132  |  -.0298462   .0428672    -0.70   0.486    -.1138643    .0541719
               133  |  -.0298687   .0432359    -0.69   0.490    -.1146094     .054872
               134  |  -.0298912   .0436051    -0.69   0.493    -.1153557    .0555733
               135  |  -.0299137    .043975    -0.68   0.496    -.1161031    .0562757
               136  |  -.0299362   .0443455    -0.68   0.500    -.1168517    .0569793
               137  |  -.0299588   .0447165    -0.67   0.503    -.1176015     .057684
               138  |  -.0299813   .0450882    -0.66   0.506    -.1183525    .0583899
               139  |  -.0300039   .0454604    -0.66   0.509    -.1191046    .0590968
               140  |  -.0300265   .0458332    -0.66   0.512    -.1198579    .0598049
               141  |  -.0300491   .0462066    -0.65   0.515    -.1206124    .0605142
               142  |  -.0300717   .0465806    -0.65   0.519     -.121368    .0612246
               143  |  -.0300944   .0469552    -0.64   0.522    -.1221248    .0619361
               144  |   -.030117   .0473303    -0.64   0.525    -.1228827    .0626487
               145  |  -.0301397   .0477061    -0.63   0.528    -.1236419    .0633625
               146  |  -.0301624   .0480824    -0.63   0.530    -.1244022    .0640774
               147  |  -.0301851   .0484593    -0.62   0.533    -.1251636    .0647934
               148  |  -.0302078   .0488368    -0.62   0.536    -.1259262    .0655106
               149  |  -.0302306   .0492149    -0.61   0.539      -.12669    .0662289
               150  |  -.0302533   .0495936    -0.61   0.542     -.127455    .0669483
               151  |  -.0302761   .0499728    -0.61   0.545    -.1282211    .0676688
               152  |  -.0302989   .0503527    -0.60   0.547    -.1289883    .0683905
               153  |  -.0303217   .0507331    -0.60   0.550    -.1297568    .0691133
               154  |  -.0303446   .0511141    -0.59   0.553    -.1305263    .0698372
               155  |  -.0303674   .0514957    -0.59   0.555    -.1312971    .0705623
               156  |  -.0303903   .0518779    -0.59   0.558     -.132069    .0712885
               157  |  -.0304131   .0522606    -0.58   0.561    -.1328421    .0720158
               158  |   -.030436    .052644    -0.58   0.563    -.1336163    .0727442
               159  |   -.030459   .0530279    -0.57   0.566    -.1343917    .0734738
               160  |  -.0304819   .0534124    -0.57   0.568    -.1351683    .0742045
               161  |  -.0305049   .0537975    -0.57   0.571     -.135946    .0749363
               162  |  -.0305278   .0541832    -0.56   0.573    -.1367249    .0756692
               163  |  -.0305508   .0545694    -0.56   0.576    -.1375049    .0764033
               164  |  -.0305738   .0549563    -0.56   0.578    -.1382861    .0771385
               165  |  -.0305968   .0553437    -0.55   0.580    -.1390685    .0778748
               166  |  -.0306199   .0557317    -0.55   0.583     -.139852    .0786123
               167  |  -.0306429   .0561203    -0.55   0.585    -.1406367    .0793508
               168  |   -.030666   .0565095    -0.54   0.587    -.1414225    .0800905
               169  |  -.0306891   .0568992    -0.54   0.590    -.1422095    .0808314
               170  |  -.0307122   .0572896    -0.54   0.592    -.1429977    .0815733
               171  |  -.0307353   .0576805    -0.53   0.594     -.143787    .0823164
               172  |  -.0307585    .058072    -0.53   0.596    -.1445775    .0830606
               173  |  -.0307816   .0584641    -0.53   0.599    -.1453692     .083806
               174  |  -.0308048   .0588568    -0.52   0.601     -.146162    .0845524
               175  |   -.030828   .0592501    -0.52   0.603     -.146956       .0853
               176  |  -.0308512   .0596439    -0.52   0.605    -.1477512    .0860488
               177  |  -.0308744   .0600384    -0.51   0.607    -.1485475    .0867986
               178  |  -.0308977   .0604334    -0.51   0.609     -.149345    .0875496
               179  |  -.0309209    .060829    -0.51   0.611    -.1501436    .0883017
               180  |  -.0309442   .0612252    -0.51   0.613    -.1509434     .089055
               181  |  -.0309675    .061622    -0.50   0.615    -.1517444    .0898094
               182  |  -.0309908   .0620194    -0.50   0.617    -.1525466    .0905649
               183  |  -.0310142   .0624173    -0.50   0.619    -.1533499    .0913215
               184  |  -.0310375   .0628159    -0.49   0.621    -.1541544    .0920793
               185  |  -.0310609    .063215    -0.49   0.623      -.15496    .0928382
               186  |  -.0310843   .0636147    -0.49   0.625    -.1557669    .0935983
               187  |  -.0311077    .064015    -0.49   0.627    -.1565749    .0943595
               188  |  -.0311311   .0644159    -0.48   0.629     -.157384    .0951218
               189  |  -.0311546   .0648174    -0.48   0.631    -.1581944    .0958852
               190  |   -.031178   .0652195    -0.48   0.633    -.1590059    .0966498
               191  |  -.0312015   .0656222    -0.48   0.634    -.1598186    .0974156
               192  |   -.031225   .0660254    -0.47   0.636    -.1606324    .0981825
               193  |  -.0312485   .0664293    -0.47   0.638    -.1614475    .0989505
               194  |   -.031272   .0668337    -0.47   0.640    -.1622637    .0997196
               195  |  -.0312956   .0672387    -0.47   0.642    -.1630811    .1004899
               196  |  -.0313191   .0676444    -0.46   0.643    -.1638996    .1012614
               197  |  -.0313427   .0680506    -0.46   0.645    -.1647194    .1020339
-------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. marginsplot, ///
>         recast(line) plot1opts(lcolor(gs8)) ciopt(color(black%20)) recastci(rarea) ///
>         ytitle("AME", size(medsmall)) ///
>         xtitle("Troops", size(medsmall))  ///
>         title("Anocracy", size(medium)) yline(0, lcolor(red)) ///
>         addplot(hist troops, /// adding histogram marginsplot graph
> blcolor(black%30) fcolor(white%10) /// bar line and fill colors
> percent /// histogram bins in "percent" rather than "discrete" (actual)
> yaxis(2) /// calls for 2nd y-axis
> yscale(alt lcolor() axis(2)) /// scaling on 2nd y-axis
> ylabel(0 "0%" 20 "20%" 40 "40%" 60 "60%" 80 "80%" 100 "100%", /// labels on 2nd y-axis
> labcolor() axis(2) tlcolor(black) tlwidth(thin) labsize(small)) /// label options on 2nd y-axis
> ytitle(" ", axis(2)))  legend(off)

Variables that uniquely identify margins: troops

. graph save "figures\Study_2\B2.4.gph", replace
file figures\Study_2\B2.4.gph saved

. 
. 
. *model 8a: New democracy
. eststo: poisson milcoupsum new_robust_democracy6##c.troops population2000 gdpcap durable conflict milexp exsol, cluster(ccode)

Iteration 0:   log pseudolikelihood = -411.78171  
Iteration 1:   log pseudolikelihood = -406.87226  
Iteration 2:   log pseudolikelihood = -403.34415  
Iteration 3:   log pseudolikelihood = -400.72867  
Iteration 4:   log pseudolikelihood = -400.69906  
Iteration 5:   log pseudolikelihood = -400.69903  

Poisson regression                                      Number of obs =  3,001
                                                        Wald chi2(9)  =  72.73
Log pseudolikelihood = -400.69903                       Prob > chi2   = 0.0000

                                                  (Std. err. adjusted for 148 clusters in ccode)
------------------------------------------------------------------------------------------------
                               |               Robust
                    milcoupsum | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------------------------+----------------------------------------------------------------
       1.new_robust_democracy6 |  -1.118677   .4094854    -2.73   0.006    -1.921254   -.3161008
                        troops |  -.1720984   .3543506    -0.49   0.627    -.8666128    .5224159
                               |
new_robust_democracy6#c.troops |
                            1  |  -.6602458   .7434278    -0.89   0.374    -2.117338     .796846
                               |
                population2000 |  -3.33e-10   6.06e-09    -0.05   0.956    -1.22e-08    1.15e-08
                        gdpcap |   -.007618   .0313899    -0.24   0.808    -.0691412    .0539051
                       durable |  -.0167517    .021227    -0.79   0.430    -.0583558    .0248523
                      conflict |   .8082747   .3447763     2.34   0.019     .1325255    1.484024
                        milexp |  -398.4421   171.2426    -2.33   0.020    -734.0715   -62.81271
                         exsol |   .7746237   2.123611     0.36   0.715    -3.387578    4.936825
                         _cons |  -2.430278    .319332    -7.61   0.000    -3.056157   -1.804399
------------------------------------------------------------------------------------------------
(est16 stored)

. margins, dydx(new_robust_democracy6) at (troops =  (0(0.05)9.8)) post

Average marginal effects                                 Number of obs = 3,001
Model VCE: Robust

Expression: Predicted number of events, predict()
dy/dx wrt:  1.new_robust_democracy6
1._at:   troops =    0
2._at:   troops =  .05
3._at:   troops =   .1
4._at:   troops =  .15
5._at:   troops =   .2
6._at:   troops =  .25
7._at:   troops =   .3
8._at:   troops =  .35
9._at:   troops =   .4
10._at:  troops =  .45
11._at:  troops =   .5
12._at:  troops =  .55
13._at:  troops =   .6
14._at:  troops =  .65
15._at:  troops =   .7
16._at:  troops =  .75
17._at:  troops =   .8
18._at:  troops =  .85
19._at:  troops =   .9
20._at:  troops =  .95
21._at:  troops =    1
22._at:  troops = 1.05
23._at:  troops =  1.1
24._at:  troops = 1.15
25._at:  troops =  1.2
26._at:  troops = 1.25
27._at:  troops =  1.3
28._at:  troops = 1.35
29._at:  troops =  1.4
30._at:  troops = 1.45
31._at:  troops =  1.5
32._at:  troops = 1.55
33._at:  troops =  1.6
34._at:  troops = 1.65
35._at:  troops =  1.7
36._at:  troops = 1.75
37._at:  troops =  1.8
38._at:  troops = 1.85
39._at:  troops =  1.9
40._at:  troops = 1.95
41._at:  troops =    2
42._at:  troops = 2.05
43._at:  troops =  2.1
44._at:  troops = 2.15
45._at:  troops =  2.2
46._at:  troops = 2.25
47._at:  troops =  2.3
48._at:  troops = 2.35
49._at:  troops =  2.4
50._at:  troops = 2.45
51._at:  troops =  2.5
52._at:  troops = 2.55
53._at:  troops =  2.6
54._at:  troops = 2.65
55._at:  troops =  2.7
56._at:  troops = 2.75
57._at:  troops =  2.8
58._at:  troops = 2.85
59._at:  troops =  2.9
60._at:  troops = 2.95
61._at:  troops =    3
62._at:  troops = 3.05
63._at:  troops =  3.1
64._at:  troops = 3.15
65._at:  troops =  3.2
66._at:  troops = 3.25
67._at:  troops =  3.3
68._at:  troops = 3.35
69._at:  troops =  3.4
70._at:  troops = 3.45
71._at:  troops =  3.5
72._at:  troops = 3.55
73._at:  troops =  3.6
74._at:  troops = 3.65
75._at:  troops =  3.7
76._at:  troops = 3.75
77._at:  troops =  3.8
78._at:  troops = 3.85
79._at:  troops =  3.9
80._at:  troops = 3.95
81._at:  troops =    4
82._at:  troops = 4.05
83._at:  troops =  4.1
84._at:  troops = 4.15
85._at:  troops =  4.2
86._at:  troops = 4.25
87._at:  troops =  4.3
88._at:  troops = 4.35
89._at:  troops =  4.4
90._at:  troops = 4.45
91._at:  troops =  4.5
92._at:  troops = 4.55
93._at:  troops =  4.6
94._at:  troops = 4.65
95._at:  troops =  4.7
96._at:  troops = 4.75
97._at:  troops =  4.8
98._at:  troops = 4.85
99._at:  troops =  4.9
100._at: troops = 4.95
101._at: troops =    5
102._at: troops = 5.05
103._at: troops =  5.1
104._at: troops = 5.15
105._at: troops =  5.2
106._at: troops = 5.25
107._at: troops =  5.3
108._at: troops = 5.35
109._at: troops =  5.4
110._at: troops = 5.45
111._at: troops =  5.5
112._at: troops = 5.55
113._at: troops =  5.6
114._at: troops = 5.65
115._at: troops =  5.7
116._at: troops = 5.75
117._at: troops =  5.8
118._at: troops = 5.85
119._at: troops =  5.9
120._at: troops = 5.95
121._at: troops =    6
122._at: troops = 6.05
123._at: troops =  6.1
124._at: troops = 6.15
125._at: troops =  6.2
126._at: troops = 6.25
127._at: troops =  6.3
128._at: troops = 6.35
129._at: troops =  6.4
130._at: troops = 6.45
131._at: troops =  6.5
132._at: troops = 6.55
133._at: troops =  6.6
134._at: troops = 6.65
135._at: troops =  6.7
136._at: troops = 6.75
137._at: troops =  6.8
138._at: troops = 6.85
139._at: troops =  6.9
140._at: troops = 6.95
141._at: troops =    7
142._at: troops = 7.05
143._at: troops =  7.1
144._at: troops = 7.15
145._at: troops =  7.2
146._at: troops = 7.25
147._at: troops =  7.3
148._at: troops = 7.35
149._at: troops =  7.4
150._at: troops = 7.45
151._at: troops =  7.5
152._at: troops = 7.55
153._at: troops =  7.6
154._at: troops = 7.65
155._at: troops =  7.7
156._at: troops = 7.75
157._at: troops =  7.8
158._at: troops = 7.85
159._at: troops =  7.9
160._at: troops = 7.95
161._at: troops =    8
162._at: troops = 8.05
163._at: troops =  8.1
164._at: troops = 8.15
165._at: troops =  8.2
166._at: troops = 8.25
167._at: troops =  8.3
168._at: troops = 8.35
169._at: troops =  8.4
170._at: troops = 8.45
171._at: troops =  8.5
172._at: troops = 8.55
173._at: troops =  8.6
174._at: troops = 8.65
175._at: troops =  8.7
176._at: troops = 8.75
177._at: troops =  8.8
178._at: troops = 8.85
179._at: troops =  8.9
180._at: troops = 8.95
181._at: troops =    9
182._at: troops = 9.05
183._at: troops =  9.1
184._at: troops = 9.15
185._at: troops =  9.2
186._at: troops = 9.25
187._at: troops =  9.3
188._at: troops = 9.35
189._at: troops =  9.4
190._at: troops = 9.45
191._at: troops =  9.5
192._at: troops = 9.55
193._at: troops =  9.6
194._at: troops = 9.65
195._at: troops =  9.7
196._at: troops = 9.75
197._at: troops =  9.8

------------------------------------------------------------------------------------------
                         |            Delta-method
                         |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------------------+----------------------------------------------------------------
0.new_robust_democracy6  |  (base outcome)
-------------------------+----------------------------------------------------------------
1.new_robust_democracy6  |
                     _at |
                      1  |  -.0334746   .0101015    -3.31   0.001    -.0532733    -.013676
                      2  |  -.0337108   .0096425    -3.50   0.000    -.0526098   -.0148118
                      3  |  -.0339236   .0092188    -3.68   0.000    -.0519921   -.0158551
                      4  |  -.0341141   .0088303    -3.86   0.000    -.0514212    -.016807
                      5  |  -.0342834   .0084779    -4.04   0.000    -.0508997   -.0176671
                      6  |  -.0344325    .008163    -4.22   0.000    -.0504317   -.0184332
                      7  |  -.0345622    .007888    -4.38   0.000    -.0500223    -.019102
                      8  |  -.0346734   .0076553    -4.53   0.000    -.0496776   -.0196692
                      9  |  -.0347671    .007468    -4.66   0.000    -.0494042   -.0201301
                     10  |  -.0348441   .0073288    -4.75   0.000    -.0492083   -.0204799
                     11  |  -.0349052   .0072401    -4.82   0.000    -.0490956   -.0207148
                     12  |   -.034951   .0072037    -4.85   0.000    -.0490701   -.0208319
                     13  |  -.0349824   .0072204    -4.84   0.000    -.0491342   -.0208306
                     14  |  -.0350001   .0072899    -4.80   0.000    -.0492881    -.020712
                     15  |  -.0350046   .0074109    -4.72   0.000    -.0495297   -.0204796
                     16  |  -.0349968   .0075809    -4.62   0.000     -.049855   -.0201385
                     17  |   -.034977   .0077967    -4.49   0.000    -.0502582   -.0196958
                     18  |  -.0349461   .0080546    -4.34   0.000    -.0507327   -.0191594
                     19  |  -.0349044   .0083505    -4.18   0.000     -.051271   -.0185378
                     20  |  -.0348526   .0086802    -4.02   0.000    -.0518655   -.0178397
                     21  |  -.0347911   .0090397    -3.85   0.000    -.0525087   -.0170736
                     22  |  -.0347205   .0094251    -3.68   0.000    -.0531935   -.0162476
                     23  |  -.0346413   .0098329    -3.52   0.000    -.0539134   -.0153692
                     24  |  -.0345538   .0102597    -3.37   0.001    -.0546625   -.0144452
                     25  |  -.0344586   .0107026    -3.22   0.001    -.0554352   -.0134819
                     26  |  -.0343559   .0111589    -3.08   0.002     -.056227   -.0124849
                     27  |  -.0342463   .0116263    -2.95   0.003    -.0570334   -.0114592
                     28  |  -.0341301   .0121026    -2.82   0.005    -.0578509   -.0104094
                     29  |  -.0340077   .0125861    -2.70   0.007     -.058676   -.0093394
                     30  |  -.0338794    .013075    -2.59   0.010    -.0595059   -.0082529
                     31  |  -.0337456   .0135679    -2.49   0.013    -.0603381    -.007153
                     32  |  -.0336065   .0140635    -2.39   0.017    -.0611704   -.0060426
                     33  |  -.0334625   .0145606    -2.30   0.022    -.0620007   -.0049243
                     34  |  -.0333139   .0150581    -2.21   0.027    -.0628273   -.0038005
                     35  |  -.0331609   .0155553    -2.13   0.033    -.0636487   -.0026731
                     36  |  -.0330038   .0160512    -2.06   0.040    -.0644635   -.0015441
                     37  |  -.0328429   .0165451    -1.99   0.047    -.0652706   -.0004151
                     38  |  -.0326784   .0170363    -1.92   0.055     -.066069    .0007122
                     39  |  -.0325105   .0175244    -1.86   0.064    -.0668576    .0018366
                     40  |  -.0323395   .0180086    -1.80   0.073    -.0676357    .0029567
                     41  |  -.0321656   .0184886    -1.74   0.082    -.0684027    .0040714
                     42  |   -.031989    .018964    -1.69   0.092    -.0691577    .0051797
                     43  |  -.0318098   .0194343    -1.64   0.102    -.0699004    .0062807
                     44  |  -.0316284   .0198992    -1.59   0.112    -.0706302    .0073734
                     45  |  -.0314447   .0203585    -1.54   0.122    -.0713467    .0084572
                     46  |  -.0312591   .0208118    -1.50   0.133    -.0720495    .0095313
                     47  |  -.0310716    .021259    -1.46   0.144    -.0727384    .0105951
                     48  |  -.0308825   .0216997    -1.42   0.155    -.0734132    .0116481
                     49  |  -.0306919   .0221339    -1.39   0.166    -.0740734    .0126897
                     50  |  -.0304999   .0225613    -1.35   0.176    -.0747191    .0137194
                     51  |  -.0303066   .0229818    -1.32   0.187    -.0753501    .0147369
                     52  |  -.0301122   .0233953    -1.29   0.198    -.0759662    .0157418
                     53  |  -.0299168   .0238018    -1.26   0.209    -.0765674    .0167338
                     54  |  -.0297205    .024201    -1.23   0.219    -.0771536    .0177125
                     55  |  -.0295235    .024593    -1.20   0.230    -.0777248    .0186778
                     56  |  -.0293258   .0249776    -1.17   0.240     -.078281    .0196294
                     57  |  -.0291276   .0253549    -1.15   0.251    -.0788222     .020567
                     58  |  -.0289289   .0257247    -1.12   0.261    -.0793484    .0214906
                     59  |  -.0287298   .0260871    -1.10   0.271    -.0798597    .0224001
                     60  |  -.0285304   .0264421    -1.08   0.281    -.0803561    .0232952
                     61  |  -.0283309   .0267897    -1.06   0.290    -.0808377     .024176
                     62  |  -.0281312   .0271298    -1.04   0.300    -.0813046    .0250423
                     63  |  -.0279314   .0274625    -1.02   0.309    -.0817569     .025894
                     64  |  -.0277317   .0277878    -1.00   0.318    -.0821948    .0267313
                     65  |  -.0275321   .0281057    -0.98   0.327    -.0826182     .027554
                     66  |  -.0273327   .0284162    -0.96   0.336    -.0830275    .0283621
                     67  |  -.0271335   .0287195    -0.94   0.345    -.0834226    .0291557
                     68  |  -.0269345   .0290155    -0.93   0.353    -.0838038    .0299347
                     69  |  -.0267359   .0293042    -0.91   0.362    -.0841712    .0306993
                     70  |  -.0265377   .0295858    -0.90   0.370    -.0845249    .0314495
                     71  |    -.02634   .0298603    -0.88   0.378    -.0848652    .0321853
                     72  |  -.0261427   .0301278    -0.87   0.386    -.0851921    .0329067
                     73  |  -.0259459   .0303883    -0.85   0.393    -.0855058     .033614
                     74  |  -.0257497   .0306418    -0.84   0.401    -.0858066    .0343072
                     75  |  -.0255542   .0308885    -0.83   0.408    -.0860946    .0349863
                     76  |  -.0253593   .0311285    -0.81   0.415      -.08637    .0356514
                     77  |  -.0251651   .0313617    -0.80   0.422    -.0866329    .0363027
                     78  |  -.0249716   .0315883    -0.79   0.429    -.0868835    .0369404
                     79  |  -.0247788   .0318083    -0.78   0.436     -.087122    .0375644
                     80  |  -.0245869   .0320219    -0.77   0.443    -.0873487    .0381749
                     81  |  -.0243957   .0322291    -0.76   0.449    -.0875636    .0387721
                     82  |  -.0242055   .0324299    -0.75   0.455     -.087767    .0393561
                     83  |   -.024016   .0326246    -0.74   0.462     -.087959    .0399269
                     84  |  -.0238275    .032813    -0.73   0.468    -.0881399    .0404849
                     85  |  -.0236399   .0329954    -0.72   0.474    -.0883098      .04103
                     86  |  -.0234532   .0331719    -0.71   0.480    -.0884688    .0415625
                     87  |  -.0232674   .0333424    -0.70   0.485    -.0886173    .0420824
                     88  |  -.0230827   .0335071    -0.69   0.491    -.0887554      .04259
                     89  |  -.0228989    .033666    -0.68   0.496    -.0888831    .0430853
                     90  |  -.0227161   .0338194    -0.67   0.502    -.0890009    .0435686
                     91  |  -.0225344   .0339671    -0.66   0.507    -.0891087    .0440399
                     92  |  -.0223537   .0341094    -0.66   0.512    -.0892068    .0444995
                     93  |   -.022174   .0342462    -0.65   0.517    -.0892954    .0449474
                     94  |  -.0219954   .0343778    -0.64   0.522    -.0893746    .0453839
                     95  |  -.0218178   .0345041    -0.63   0.527    -.0894446     .045809
                     96  |  -.0216413   .0346253    -0.63   0.532    -.0895056     .046223
                     97  |  -.0214659   .0347414    -0.62   0.537    -.0895578    .0466259
                     98  |  -.0212917   .0348525    -0.61   0.541    -.0896013     .047018
                     99  |  -.0211185   .0349587    -0.60   0.546    -.0896363    .0473994
                    100  |  -.0209464   .0350601    -0.60   0.550    -.0896629    .0477702
                    101  |  -.0207754   .0351567    -0.59   0.555    -.0896813    .0481305
                    102  |  -.0206055   .0352487    -0.58   0.559    -.0896917    .0484807
                    103  |  -.0204368   .0353361    -0.58   0.563    -.0896943    .0488207
                    104  |  -.0202692    .035419    -0.57   0.567    -.0896891    .0491507
                    105  |  -.0201027   .0354974    -0.57   0.571    -.0896764     .049471
                    106  |  -.0199374   .0355715    -0.56   0.575    -.0896563    .0497815
                    107  |  -.0197732   .0356413    -0.55   0.579    -.0896289    .0500826
                    108  |  -.0196101    .035707    -0.55   0.583    -.0895945    .0503742
                    109  |  -.0194482   .0357684    -0.54   0.587     -.089553    .0506567
                    110  |  -.0192874   .0358259    -0.54   0.590    -.0895048      .05093
                    111  |  -.0191277   .0358793    -0.53   0.594    -.0894498    .0511944
                    112  |  -.0189692   .0359288    -0.53   0.598    -.0893884    .0514499
                    113  |  -.0188118   .0359745    -0.52   0.601    -.0893205    .0516968
                    114  |  -.0186556   .0360164    -0.52   0.604    -.0892464    .0519352
                    115  |  -.0185005   .0360546    -0.51   0.608    -.0891662    .0521651
                    116  |  -.0183466   .0360891    -0.51   0.611    -.0890799    .0523868
                    117  |  -.0181937   .0361201    -0.50   0.614    -.0889878    .0526004
                    118  |   -.018042   .0361476    -0.50   0.618      -.08889    .0528059
                    119  |  -.0178915   .0361716    -0.49   0.621    -.0887865    .0530036
                    120  |   -.017742   .0361923    -0.49   0.624    -.0886776    .0531935
                    121  |  -.0175937   .0362096    -0.49   0.627    -.0885632    .0533758
                    122  |  -.0174465   .0362237    -0.48   0.630    -.0884437    .0535507
                    123  |  -.0173004   .0362346    -0.48   0.633    -.0883189    .0537181
                    124  |  -.0171555   .0362424    -0.47   0.636    -.0881892    .0538783
                    125  |  -.0170116   .0362471    -0.47   0.639    -.0880546    .0540314
                    126  |  -.0168688   .0362488    -0.47   0.642    -.0879151    .0541774
                    127  |  -.0167272   .0362475    -0.46   0.644     -.087771    .0543166
                    128  |  -.0165866   .0362434    -0.46   0.647    -.0876223     .054449
                    129  |  -.0164472   .0362364    -0.45   0.650    -.0874691    .0545748
                    130  |  -.0163088   .0362266    -0.45   0.653    -.0873116     .054694
                    131  |  -.0161715   .0362141    -0.45   0.655    -.0871497    .0548068
                    132  |  -.0160353   .0361989    -0.44   0.658    -.0869838    .0549132
                    133  |  -.0159001   .0361811    -0.44   0.660    -.0868137    .0550135
                    134  |   -.015766   .0361607    -0.44   0.663    -.0866397    .0551076
                    135  |   -.015633   .0361378    -0.43   0.665    -.0864618    .0551957
                    136  |  -.0155011   .0361124    -0.43   0.668    -.0862801     .055278
                    137  |  -.0153701   .0360846    -0.43   0.670    -.0860947    .0553544
                    138  |  -.0152403   .0360545    -0.42   0.673    -.0859057    .0554252
                    139  |  -.0151114    .036022    -0.42   0.675    -.0857132    .0554903
                    140  |  -.0149836   .0359872    -0.42   0.677    -.0855173      .05555
                    141  |  -.0148569   .0359502    -0.41   0.679     -.085318    .0556043
                    142  |  -.0147311    .035911    -0.41   0.682    -.0851155    .0556532
                    143  |  -.0146064   .0358697    -0.41   0.684    -.0849098     .055697
                    144  |  -.0144826   .0358263    -0.40   0.686     -.084701    .0557357
                    145  |  -.0143599   .0357809    -0.40   0.688    -.0844892    .0557693
                    146  |  -.0142382   .0357334    -0.40   0.690    -.0842744     .055798
                    147  |  -.0141175    .035684    -0.40   0.692    -.0840568    .0558219
                    148  |  -.0139977   .0356326    -0.39   0.694    -.0838364     .055841
                    149  |  -.0138789   .0355794    -0.39   0.696    -.0836133    .0558554
                    150  |  -.0137611   .0355243    -0.39   0.698    -.0833875    .0558653
                    151  |  -.0136443   .0354675    -0.38   0.700    -.0831592    .0558707
                    152  |  -.0135284   .0354088    -0.38   0.702    -.0829284    .0558716
                    153  |  -.0134134   .0353484    -0.38   0.704    -.0826951    .0558682
                    154  |  -.0132994   .0352864    -0.38   0.706    -.0824595    .0558606
                    155  |  -.0131864   .0352227    -0.37   0.708    -.0822216    .0558488
                    156  |  -.0130743   .0351574    -0.37   0.710    -.0819815    .0558329
                    157  |  -.0129631   .0350905    -0.37   0.712    -.0817391     .055813
                    158  |  -.0128528    .035022    -0.37   0.714    -.0814947    .0557891
                    159  |  -.0127434   .0349521    -0.36   0.715    -.0812483    .0557614
                    160  |   -.012635   .0348807    -0.36   0.717    -.0809998    .0557299
                    161  |  -.0125274   .0348078    -0.36   0.719    -.0807495    .0556946
                    162  |  -.0124208   .0347335    -0.36   0.721    -.0804972    .0556557
                    163  |   -.012315   .0346579    -0.36   0.722    -.0802432    .0556132
                    164  |  -.0122101   .0345809    -0.35   0.724    -.0799874    .0555672
                    165  |  -.0121061   .0345026    -0.35   0.726    -.0797299    .0555178
                    166  |  -.0120029    .034423    -0.35   0.727    -.0794708     .055465
                    167  |  -.0119006   .0343422    -0.35   0.729    -.0792101    .0554088
                    168  |  -.0117992   .0342601    -0.34   0.731    -.0789478    .0553495
                    169  |  -.0116986   .0341769    -0.34   0.732     -.078684    .0552869
                    170  |  -.0115988   .0340925    -0.34   0.734    -.0784189    .0552212
                    171  |  -.0114999   .0340069    -0.34   0.735    -.0781523    .0551525
                    172  |  -.0114018   .0339203    -0.34   0.737    -.0778844    .0550807
                    173  |  -.0113045   .0338326    -0.33   0.738    -.0776152    .0550061
                    174  |  -.0112081   .0337438    -0.33   0.740    -.0773447    .0549285
                    175  |  -.0111125    .033654    -0.33   0.741    -.0770731    .0548481
                    176  |  -.0110176   .0335632    -0.33   0.743    -.0768002     .054765
                    177  |  -.0109236   .0334714    -0.33   0.744    -.0765263    .0546792
                    178  |  -.0108303   .0333787    -0.32   0.746    -.0762513    .0545907
                    179  |  -.0107379    .033285    -0.32   0.747    -.0759753    .0544996
                    180  |  -.0106462   .0331905    -0.32   0.748    -.0756984     .054406
                    181  |  -.0105553   .0330951    -0.32   0.750    -.0754204    .0543099
                    182  |  -.0104651   .0329988    -0.32   0.751    -.0751416    .0542113
                    183  |  -.0103757   .0329017    -0.32   0.752    -.0748619    .0541104
                    184  |  -.0102871   .0328038    -0.31   0.754    -.0745814    .0540071
                    185  |  -.0101992   .0327051    -0.31   0.755    -.0743001    .0539016
                    186  |  -.0101121   .0326057    -0.31   0.756     -.074018    .0537938
                    187  |  -.0100257   .0325055    -0.31   0.758    -.0737352    .0536839
                    188  |    -.00994   .0324046    -0.31   0.759    -.0734518    .0535718
                    189  |  -.0098551    .032303    -0.31   0.760    -.0731677    .0534576
                    190  |  -.0097708   .0322007    -0.30   0.762     -.072883    .0533414
                    191  |  -.0096873   .0320978    -0.30   0.763    -.0725978    .0532231
                    192  |  -.0096045   .0319942    -0.30   0.764     -.072312     .053103
                    193  |  -.0095224     .03189    -0.30   0.765    -.0720257    .0529809
                    194  |   -.009441   .0317852    -0.30   0.766    -.0717389    .0528569
                    195  |  -.0093603   .0316799    -0.30   0.768    -.0714517    .0527312
                    196  |  -.0092802    .031574    -0.29   0.769    -.0711641    .0526036
                    197  |  -.0092009   .0314675    -0.29   0.770    -.0708761    .0524743
------------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. marginsplot, ///
>         recast(line) plot1opts(lcolor(gs8)) ciopt(color(black%20)) recastci(rarea) ///
>         ytitle("AME", size(medsmall)) ///
>         xtitle("Troops", size(medsmall))  ///
>         title("New democracy", size(medium)) yline(0, lcolor(red)) ///
>         addplot(hist troops, /// adding histogram marginsplot graph
> blcolor(black%30) fcolor(white%10) /// bar line and fill colors
> percent /// histogram bins in "percent" rather than "discrete" (actual)
> yaxis(2) /// calls for 2nd y-axis
> yscale(alt lcolor() axis(2)) /// scaling on 2nd y-axis
> ylabel(0 "0%" 20 "20%" 40 "40%" 60 "60%" 80 "80%" 100 "100%", /// labels on 2nd y-axis
> labcolor() axis(2) tlcolor(black) tlwidth(thin) labsize(small)) /// label options on 2nd y-axis
> ytitle(" ", axis(2)))  legend(off) 

Variables that uniquely identify margins: troops

. graph save "figures\Study_2\B2.8.gph", replace
file figures\Study_2\B2.8.gph saved

. 
. 
. graph combine "figures\Study_2\B2.2.gph" "figures\Study_2\B2.4.gph" "figures\Study_2\B2.8.gph", col(3) 

. graph save "figures\Study_2\Figure_B3_AME_by_troop_rounded.gph", replace
file figures\Study_2\Figure_B3_AME_by_troop_rounded.gph saved

. 
end of do-file

. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

.         
. *model 2a: autocracy
. poisson milcoupsum robust_autocracy6##c.troops population2000 gdpcap durable conflict milexp exsol, cluster(ccode)

Iteration 0:   log pseudolikelihood = -449.22112  
Iteration 1:   log pseudolikelihood = -410.06911  
Iteration 2:   log pseudolikelihood = -404.01836  
Iteration 3:   log pseudolikelihood = -402.11343  
Iteration 4:   log pseudolikelihood = -402.01812  
Iteration 5:   log pseudolikelihood = -402.01764  
Iteration 6:   log pseudolikelihood = -402.01764  

Poisson regression                                      Number of obs =  3,001
                                                        Wald chi2(9)  =  82.80
Log pseudolikelihood = -402.01764                       Prob > chi2   = 0.0000

                                              (Std. err. adjusted for 148 clusters in ccode)
--------------------------------------------------------------------------------------------
                           |               Robust
                milcoupsum | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------------+----------------------------------------------------------------
       1.robust_autocracy6 |   .5278635   .3128468     1.69   0.092    -.0853049    1.141032
                    troops |  -1.838259   1.027145    -1.79   0.074    -3.851427    .1749095
                           |
robust_autocracy6#c.troops |
                        1  |   2.053367   1.009086     2.03   0.042     .0755952    4.031138
                           |
            population2000 |  -2.34e-09   6.43e-09    -0.36   0.716    -1.49e-08    1.03e-08
                    gdpcap |   -.020064   .0477717    -0.42   0.674    -.1136947    .0735667
                   durable |  -.0224258   .0221588    -1.01   0.312    -.0658563    .0210046
                  conflict |   .9358275   .3751772     2.49   0.013     .2004936    1.671161
                    milexp |  -378.6024   181.1369    -2.09   0.037    -733.6242   -23.58055
                     exsol |   .9254581   2.147148     0.43   0.666    -3.282875    5.133792
                     _cons |  -2.714514   .3781303    -7.18   0.000    -3.455636   -1.973392
--------------------------------------------------------------------------------------------

. margins, dydx(robust_autocracy6) at (troops = (0(0.05)9.8)) post

Average marginal effects                                 Number of obs = 3,001
Model VCE: Robust

Expression: Predicted number of events, predict()
dy/dx wrt:  1.robust_autocracy6
1._at:   troops =    0
2._at:   troops =  .05
3._at:   troops =   .1
4._at:   troops =  .15
5._at:   troops =   .2
6._at:   troops =  .25
7._at:   troops =   .3
8._at:   troops =  .35
9._at:   troops =   .4
10._at:  troops =  .45
11._at:  troops =   .5
12._at:  troops =  .55
13._at:  troops =   .6
14._at:  troops =  .65
15._at:  troops =   .7
16._at:  troops =  .75
17._at:  troops =   .8
18._at:  troops =  .85
19._at:  troops =   .9
20._at:  troops =  .95
21._at:  troops =    1
22._at:  troops = 1.05
23._at:  troops =  1.1
24._at:  troops = 1.15
25._at:  troops =  1.2
26._at:  troops = 1.25
27._at:  troops =  1.3
28._at:  troops = 1.35
29._at:  troops =  1.4
30._at:  troops = 1.45
31._at:  troops =  1.5
32._at:  troops = 1.55
33._at:  troops =  1.6
34._at:  troops = 1.65
35._at:  troops =  1.7
36._at:  troops = 1.75
37._at:  troops =  1.8
38._at:  troops = 1.85
39._at:  troops =  1.9
40._at:  troops = 1.95
41._at:  troops =    2
42._at:  troops = 2.05
43._at:  troops =  2.1
44._at:  troops = 2.15
45._at:  troops =  2.2
46._at:  troops = 2.25
47._at:  troops =  2.3
48._at:  troops = 2.35
49._at:  troops =  2.4
50._at:  troops = 2.45
51._at:  troops =  2.5
52._at:  troops = 2.55
53._at:  troops =  2.6
54._at:  troops = 2.65
55._at:  troops =  2.7
56._at:  troops = 2.75
57._at:  troops =  2.8
58._at:  troops = 2.85
59._at:  troops =  2.9
60._at:  troops = 2.95
61._at:  troops =    3
62._at:  troops = 3.05
63._at:  troops =  3.1
64._at:  troops = 3.15
65._at:  troops =  3.2
66._at:  troops = 3.25
67._at:  troops =  3.3
68._at:  troops = 3.35
69._at:  troops =  3.4
70._at:  troops = 3.45
71._at:  troops =  3.5
72._at:  troops = 3.55
73._at:  troops =  3.6
74._at:  troops = 3.65
75._at:  troops =  3.7
76._at:  troops = 3.75
77._at:  troops =  3.8
78._at:  troops = 3.85
79._at:  troops =  3.9
80._at:  troops = 3.95
81._at:  troops =    4
82._at:  troops = 4.05
83._at:  troops =  4.1
84._at:  troops = 4.15
85._at:  troops =  4.2
86._at:  troops = 4.25
87._at:  troops =  4.3
88._at:  troops = 4.35
89._at:  troops =  4.4
90._at:  troops = 4.45
91._at:  troops =  4.5
92._at:  troops = 4.55
93._at:  troops =  4.6
94._at:  troops = 4.65
95._at:  troops =  4.7
96._at:  troops = 4.75
97._at:  troops =  4.8
98._at:  troops = 4.85
99._at:  troops =  4.9
100._at: troops = 4.95
101._at: troops =    5
102._at: troops = 5.05
103._at: troops =  5.1
104._at: troops = 5.15
105._at: troops =  5.2
106._at: troops = 5.25
107._at: troops =  5.3
108._at: troops = 5.35
109._at: troops =  5.4
110._at: troops = 5.45
111._at: troops =  5.5
112._at: troops = 5.55
113._at: troops =  5.6
114._at: troops = 5.65
115._at: troops =  5.7
116._at: troops = 5.75
117._at: troops =  5.8
118._at: troops = 5.85
119._at: troops =  5.9
120._at: troops = 5.95
121._at: troops =    6
122._at: troops = 6.05
123._at: troops =  6.1
124._at: troops = 6.15
125._at: troops =  6.2
126._at: troops = 6.25
127._at: troops =  6.3
128._at: troops = 6.35
129._at: troops =  6.4
130._at: troops = 6.45
131._at: troops =  6.5
132._at: troops = 6.55
133._at: troops =  6.6
134._at: troops = 6.65
135._at: troops =  6.7
136._at: troops = 6.75
137._at: troops =  6.8
138._at: troops = 6.85
139._at: troops =  6.9
140._at: troops = 6.95
141._at: troops =    7
142._at: troops = 7.05
143._at: troops =  7.1
144._at: troops = 7.15
145._at: troops =  7.2
146._at: troops = 7.25
147._at: troops =  7.3
148._at: troops = 7.35
149._at: troops =  7.4
150._at: troops = 7.45
151._at: troops =  7.5
152._at: troops = 7.55
153._at: troops =  7.6
154._at: troops = 7.65
155._at: troops =  7.7
156._at: troops = 7.75
157._at: troops =  7.8
158._at: troops = 7.85
159._at: troops =  7.9
160._at: troops = 7.95
161._at: troops =    8
162._at: troops = 8.05
163._at: troops =  8.1
164._at: troops = 8.15
165._at: troops =  8.2
166._at: troops = 8.25
167._at: troops =  8.3
168._at: troops = 8.35
169._at: troops =  8.4
170._at: troops = 8.45
171._at: troops =  8.5
172._at: troops = 8.55
173._at: troops =  8.6
174._at: troops = 8.65
175._at: troops =  8.7
176._at: troops = 8.75
177._at: troops =  8.8
178._at: troops = 8.85
179._at: troops =  8.9
180._at: troops = 8.95
181._at: troops =    9
182._at: troops = 9.05
183._at: troops =  9.1
184._at: troops = 9.15
185._at: troops =  9.2
186._at: troops = 9.25
187._at: troops =  9.3
188._at: troops = 9.35
189._at: troops =  9.4
190._at: troops = 9.45
191._at: troops =  9.5
192._at: troops = 9.55
193._at: troops =  9.6
194._at: troops = 9.65
195._at: troops =  9.7
196._at: troops = 9.75
197._at: troops =  9.8

--------------------------------------------------------------------------------------
                     |            Delta-method
                     |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
---------------------+----------------------------------------------------------------
0.robust_autocracy6  |  (base outcome)
---------------------+----------------------------------------------------------------
1.robust_autocracy6  |
                 _at |
                  1  |   .0240775   .0166719     1.44   0.149    -.0085989    .0567539
                  2  |   .0277532   .0164575     1.69   0.092    -.0045028    .0600093
                  3  |   .0311688   .0164246     1.90   0.058    -.0010229    .0633605
                  4  |   .0343477   .0164992     2.08   0.037       .00201    .0666855
                  5  |   .0373115    .016629     2.24   0.025     .0047192    .0699037
                  6  |   .0400796   .0167799     2.39   0.017     .0071917    .0729675
                  7  |     .04267   .0169309     2.52   0.012      .009486     .075854
                  8  |    .045099   .0170705     2.64   0.008     .0116414    .0785566
                  9  |   .0473815   .0171934     2.76   0.006     .0136831    .0810799
                 10  |    .049531   .0172982     2.86   0.004     .0156271     .083435
                 11  |   .0515601   .0173863     2.97   0.003     .0174835    .0856366
                 12  |   .0534799   .0174603     3.06   0.002     .0192584    .0877014
                 13  |   .0553008   .0175235     3.16   0.002     .0209553    .0896464
                 14  |   .0570324   .0175799     3.24   0.001     .0225765    .0914883
                 15  |   .0586831   .0176329     3.33   0.001     .0241232     .093243
                 16  |   .0602608   .0176862     3.41   0.001     .0255965    .0949252
                 17  |   .0617728   .0177429     3.48   0.000     .0269973    .0965483
                 18  |   .0632256   .0178059     3.55   0.000     .0283266    .0981245
                 19  |   .0646252   .0178776     3.61   0.000     .0295857    .0996646
                 20  |    .065977     .01796     3.67   0.000      .030776     .101178
                 21  |   .0672861   .0180549     3.73   0.000     .0318992    .1026731
                 22  |   .0685571   .0181636     3.77   0.000     .0329571    .1041571
                 23  |   .0697941   .0182872     3.82   0.000     .0339518    .1056364
                 24  |   .0710009   .0184265     3.85   0.000     .0348856    .1071162
                 25  |   .0721811   .0185821     3.88   0.000     .0357608    .1086014
                 26  |   .0733378   .0187544     3.91   0.000     .0365798    .1100958
                 27  |    .074474   .0189436     3.93   0.000     .0373452    .1116029
                 28  |   .0755924   .0191499     3.95   0.000     .0380592    .1131256
                 29  |   .0766953   .0193733     3.96   0.000     .0387244    .1146663
                 30  |   .0777852   .0196137     3.97   0.000      .039343    .1162273
                 31  |   .0788639    .019871     3.97   0.000     .0399173    .1178104
                 32  |   .0799334   .0201452     3.97   0.000     .0404495    .1194173
                 33  |   .0809955   .0204361     3.96   0.000     .0409415    .1210494
                 34  |   .0820516   .0207434     3.96   0.000     .0413953     .122708
                 35  |   .0831034   .0210671     3.94   0.000     .0418126    .1243942
                 36  |   .0841521   .0214071     3.93   0.000      .042195    .1261091
                 37  |   .0851989   .0217631     3.91   0.000     .0425441    .1278538
                 38  |   .0862451   .0221351     3.90   0.000     .0428611     .129629
                 39  |   .0872916    .022523     3.88   0.000     .0431474    .1314358
                 40  |   .0883394   .0229266     3.85   0.000      .043404    .1332748
                 41  |   .0893894   .0233461     3.83   0.000      .043632    .1351469
                 42  |   .0904425   .0237812     3.80   0.000     .0438322    .1370528
                 43  |   .0914993   .0242321     3.78   0.000     .0440053    .1389933
                 44  |   .0925607   .0246986     3.75   0.000     .0441522    .1409691
                 45  |   .0936272   .0251809     3.72   0.000     .0442734    .1429809
                 46  |   .0946995    .025679     3.69   0.000     .0443695    .1450295
                 47  |   .0957781    .026193     3.66   0.000     .0444408    .1471154
                 48  |   .0968636   .0267228     3.62   0.000     .0444879    .1492393
                 49  |   .0979565   .0272687     3.59   0.000     .0445109     .151402
                 50  |   .0990571   .0278306     3.56   0.000     .0445102    .1536041
                 51  |   .1001661   .0284088     3.53   0.000      .044486    .1558462
                 52  |   .1012837   .0290032     3.49   0.000     .0444384     .158129
                 53  |   .1024104   .0296142     3.46   0.001     .0443677    .1604531
                 54  |   .1035465   .0302417     3.42   0.001     .0442738    .1628192
                 55  |   .1046924    .030886     3.39   0.001     .0441569    .1652278
                 56  |   .1058483   .0315472     3.36   0.001     .0440169    .1676797
                 57  |   .1070147   .0322255     3.32   0.001     .0438539    .1701754
                 58  |   .1081917   .0329209     3.29   0.001     .0436679    .1727156
                 59  |   .1093798   .0336338     3.25   0.001     .0434587    .1753008
                 60  |   .1105791   .0343643     3.22   0.001     .0432264    .1779318
                 61  |   .1117899   .0351125     3.18   0.001     .0429707    .1806091
                 62  |   .1130125   .0358786     3.15   0.002     .0426917    .1833333
                 63  |   .1142471   .0366629     3.12   0.002     .0423891     .186105
                 64  |   .1154939   .0374655     3.08   0.002     .0420629     .188925
                 65  |   .1167533   .0382867     3.05   0.002     .0417128    .1917938
                 66  |   .1180253   .0391266     3.02   0.003     .0413386    .1947119
                 67  |   .1193102   .0399854     2.98   0.003     .0409403    .1976802
                 68  |   .1206083   .0408634     2.95   0.003     .0405175    .2006991
                 69  |   .1219197   .0417608     2.92   0.004     .0400701    .2037693
                 70  |   .1232447   .0426777     2.89   0.004     .0395979    .2068915
                 71  |   .1245834   .0436145     2.86   0.004     .0391005    .2100663
                 72  |   .1259361   .0445714     2.83   0.005     .0385778    .2132944
                 73  |   .1273029   .0455485     2.79   0.005     .0380294    .2165763
                 74  |    .128684   .0465462     2.76   0.006     .0374551    .2199129
                 75  |   .1300797   .0475647     2.73   0.006     .0368547    .2233047
                 76  |   .1314901   .0486041     2.71   0.007     .0362278    .2267524
                 77  |   .1329154   .0496649     2.68   0.007     .0355741    .2302568
                 78  |   .1343559   .0507471     2.65   0.008     .0348933    .2338184
                 79  |   .1358116   .0518512     2.62   0.009     .0341851    .2374381
                 80  |   .1372828   .0529773     2.59   0.010     .0334492    .2411165
                 81  |   .1387697   .0541258     2.56   0.010     .0326852    .2448543
                 82  |   .1402725   .0552968     2.54   0.011     .0318927    .2486523
                 83  |   .1417913   .0564908     2.51   0.012     .0310714    .2525112
                 84  |   .1433264   .0577079     2.48   0.013      .030221    .2564318
                 85  |   .1448779   .0589485     2.46   0.014      .029341    .2604147
                 86  |    .146446   .0602128     2.43   0.015     .0284311    .2644609
                 87  |    .148031   .0615012     2.41   0.016     .0274909    .2685711
                 88  |    .149633   .0628139     2.38   0.017     .0265199     .272746
                 89  |   .1512522   .0641514     2.36   0.018     .0255178    .2769866
                 90  |   .1528888   .0655138     2.33   0.020     .0244841    .2812935
                 91  |    .154543   .0669015     2.31   0.021     .0234184    .2856676
                 92  |    .156215   .0683149     2.29   0.022     .0223203    .2901098
                 93  |    .157905   .0697543     2.26   0.024     .0211892    .2946209
                 94  |   .1596133   .0712199     2.24   0.025     .0200248    .2992018
                 95  |   .1613399   .0727123     2.22   0.026     .0188265    .3038533
                 96  |   .1630851   .0742316     2.20   0.028     .0175939    .3085764
                 97  |   .1648492   .0757783     2.18   0.030     .0163265    .3133719
                 98  |   .1666323   .0773527     2.15   0.031     .0150238    .3182408
                 99  |   .1684346   .0789552     2.13   0.033     .0136852     .323184
                100  |   .1702564   .0805862     2.11   0.035     .0123104    .3282024
                101  |   .1720978    .082246     2.09   0.036     .0108986    .3332971
                102  |   .1739592   .0839351     2.07   0.038     .0094494    .3384689
                103  |   .1758406   .0856537     2.05   0.040     .0079623    .3437188
                104  |   .1777423   .0874024     2.03   0.042     .0064367    .3490479
                105  |   .1796646   .0891815     2.01   0.044      .004872    .3544572
                106  |   .1816076   .0909915     2.00   0.046     .0032676    .3599477
                107  |   .1835716   .0928327     1.98   0.048     .0016229    .3655204
                108  |   .1855569   .0947055     1.96   0.050    -.0000626    .3711763
                109  |   .1875636   .0966105     1.94   0.052    -.0017896    .3769167
                110  |   .1895919    .098548     1.92   0.054    -.0035586    .3827425
                111  |   .1916422   .1005185     1.91   0.057    -.0053704    .3886549
                112  |   .1937147   .1025224     1.89   0.059    -.0072256     .394655
                113  |   .1958095   .1045602     1.87   0.061    -.0091247    .4007438
                114  |    .197927   .1066324     1.86   0.063    -.0110686    .4069226
                115  |   .2000674   .1087393     1.84   0.066    -.0130578    .4131926
                116  |   .2022309   .1108816     1.82   0.068     -.015093    .4195549
                117  |   .2044178   .1130596     1.81   0.071     -.017175    .4260107
                118  |   .2066284   .1152739     1.79   0.073    -.0193044    .4325611
                119  |   .2088628    .117525     1.78   0.076     -.021482    .4392076
                120  |   .2111214   .1198134     1.76   0.078    -.0237085    .4459513
                121  |   .2134044   .1221395     1.75   0.081    -.0259847    .4527935
                122  |   .2157121    .124504     1.73   0.083    -.0283112    .4597354
                123  |   .2180447   .1269073     1.72   0.086    -.0306889    .4667784
                124  |   .2204026   .1293499     1.70   0.088    -.0331186    .4739238
                125  |   .2227859   .1318325     1.69   0.091     -.035601    .4811729
                126  |   .2251951   .1343555     1.68   0.094    -.0381369    .4885271
                127  |   .2276303   .1369196     1.66   0.096    -.0407273    .4959878
                128  |   .2300917   .1395253     1.65   0.099    -.0433728    .5035562
                129  |   .2325799   .1421731     1.64   0.102    -.0460743     .511234
                130  |   .2350949   .1448637     1.62   0.105    -.0488327    .5190224
                131  |   .2376371   .1475976     1.61   0.107    -.0516489    .5269231
                132  |   .2402068   .1503755     1.60   0.110    -.0545237    .5349373
                133  |   .2428043   .1531979     1.58   0.113    -.0574581    .5430666
                134  |   .2454298   .1560655     1.57   0.116    -.0604529    .5513125
                135  |   .2480838   .1589788     1.56   0.119     -.063509    .5596766
                136  |   .2507665   .1619386     1.55   0.121    -.0666275    .5681604
                137  |   .2534781   .1649455     1.54   0.124    -.0698092    .5767654
                138  |   .2562191   .1680001     1.53   0.127    -.0730551    .5854933
                139  |   .2589897   .1711031     1.51   0.130    -.0763662    .5943457
                140  |   .2617903   .1742551     1.50   0.133    -.0797435    .6033241
                141  |   .2646212   .1774569     1.49   0.136     -.083188    .6124303
                142  |   .2674827   .1807091     1.48   0.139    -.0867007     .621666
                143  |   .2703751   .1840124     1.47   0.142    -.0902827    .6310328
                144  |   .2732988   .1873676     1.46   0.145     -.093935    .6405325
                145  |   .2762541   .1907753     1.45   0.148    -.0976586    .6501667
                146  |   .2792413   .1942362     1.44   0.151    -.1014547    .6599374
                147  |   .2822609   .1977512     1.43   0.153    -.1053244    .6698462
                148  |   .2853131    .201321     1.42   0.156    -.1092688     .679895
                149  |   .2883983   .2049463     1.41   0.159    -.1132889    .6900856
                150  |   .2915169   .2086278     1.40   0.162     -.117386    .7004198
                151  |   .2946692   .2123664     1.39   0.165    -.1215613    .7108997
                152  |   .2978556   .2161629     1.38   0.168    -.1258159    .7215271
                153  |   .3010765    .220018     1.37   0.171    -.1301509    .7323038
                154  |   .3043321   .2239326     1.36   0.174    -.1345677     .743232
                155  |    .307623   .2279075     1.35   0.177    -.1390674    .7543135
                156  |   .3109495   .2319435     1.34   0.180    -.1436514    .7655504
                157  |   .3143119   .2360415     1.33   0.183    -.1483209    .7769447
                158  |   .3177107   .2402022     1.32   0.186     -.153077    .7884985
                159  |   .3211463   .2444267     1.31   0.189    -.1579213    .8002139
                160  |    .324619   .2487158     1.31   0.192     -.162855    .8120929
                161  |   .3281292   .2530703     1.30   0.195    -.1678794    .8241378
                162  |   .3316774   .2574912     1.29   0.198    -.1729959    .8363508
                163  |    .335264   .2619793     1.28   0.201     -.178206     .848734
                164  |   .3388893   .2665356     1.27   0.204    -.1835108    .8612895
                165  |   .3425539   .2711611     1.26   0.206     -.188912    .8740198
                166  |   .3462581   .2758567     1.26   0.209     -.194411    .8869272
                167  |   .3500024   .2806233     1.25   0.212    -.2000092     .900014
                168  |   .3537871    .285462     1.24   0.215    -.2057081    .9132824
                169  |   .3576127   .2903736     1.23   0.218    -.2115091    .9267345
                170  |   .3614798   .2953594     1.22   0.221    -.2174139    .9403735
                171  |   .3653886   .3004201     1.22   0.224     -.223424    .9542013
                172  |   .3693397    .305557     1.21   0.227     -.229541    .9682205
                173  |   .3733336    .310771     1.20   0.230    -.2357664    .9824336
                174  |   .3773706   .3160631     1.19   0.232    -.2421017    .9968428
                175  |   .3814513   .3214345     1.19   0.235    -.2485488    1.011451
                176  |   .3855761   .3268863     1.18   0.238    -.2551092    1.026261
                177  |   .3897455   .3324195     1.17   0.241    -.2617847    1.041276
                178  |     .39396   .3380352     1.17   0.244    -.2685769    1.056497
                179  |     .39822   .3437346     1.16   0.247    -.2754874    1.071927
                180  |   .4025261   .3495189     1.15   0.249    -.2825182    1.087571
                181  |   .4068789   .3553891     1.14   0.252     -.289671    1.103429
                182  |   .4112786   .3613466     1.14   0.255    -.2969476    1.119505
                183  |    .415726   .3673924     1.13   0.258    -.3043498    1.135802
                184  |   .4202214   .3735276     1.13   0.261    -.3118794    1.152322
                185  |   .4247654   .3797538     1.12   0.263    -.3195384    1.169069
                186  |   .4293586    .386072     1.11   0.266    -.3273286    1.186046
                187  |   .4340015   .3924835     1.11   0.269     -.335252    1.203255
                188  |   .4386945   .3989895     1.10   0.272    -.3433105      1.2207
                189  |   .4434383   .4055913     1.09   0.274     -.351506    1.238383
                190  |   .4482334   .4122903     1.09   0.277    -.3598408    1.256308
                191  |   .4530803   .4190878     1.08   0.280    -.3683167    1.274477
                192  |   .4579797   .4259851     1.08   0.282    -.3769358    1.292895
                193  |   .4629321   .4329836     1.07   0.285    -.3857002    1.311564
                194  |   .4679379   .4400845     1.06   0.288    -.3946119    1.330488
                195  |   .4729979   .4472895     1.06   0.290    -.4036734    1.349669
                196  |   .4781127   .4545999     1.05   0.293    -.4128867    1.369112
                197  |   .4832828    .462017     1.05   0.296    -.4222539    1.388819
--------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. marginsplot, ///
>         recast(line) plot1opts(lcolor(gs8)) ciopt(color(black%20)) recastci(rarea) ///
>         ytitle("AME", size(medsmall)) ///
>         xtitle("Troops", size(medsmall))  ///
>         title("Autocracy", size(medium)) yline(0, lcolor(red)) ///
>         addplot(hist troops, /// adding histogram marginsplot graph
> blcolor(black%30) fcolor(white%10) /// bar line and fill colors
> percent /// histogram bins in "percent" rather than "discrete" (actual)
> yaxis(2) /// calls for 2nd y-axis
> yscale(alt lcolor() axis(2)) /// scaling on 2nd y-axis
> ylabel(0 "0%" 20 "20%" 40 "40%" 60 "60%" 80 "80%" 100 "100%", /// labels on 2nd y-axis
> labcolor() axis(2) tlcolor(black) tlwidth(thin) labsize(small)) /// label options on 2nd y-axis
> ytitle(" ", axis(2)))  legend(off)

Variables that uniquely identify margins: troops

. graph save "figures\Study_2\B2.2.gph", replace
file figures\Study_2\B2.2.gph saved

. 
end of do-file

. 
. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. graph combine "figures\Study_2\B2.2.gph" "figures\Study_2\B2.4.gph" "figures\Study_2\B2.8.gph", col(3) title("C")

. 
end of do-file

. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. graph combine "figures\Study_2\B2.2.gph" "figures\Study_2\B2.4.gph" "figures\Study_2\B2.8.gph", col(3) title("C" position(11))

. 
end of do-file

. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. graph combine "figures\Study_2\B2.2.gph" "figures\Study_2\B2.4.gph" "figures\Study_2\B2.8.gph", col(3) title("C") position(11)
option position() not allowed
r(198);

end of do-file

r(198);

. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. graph combine "figures\Study_2\B2.2.gph" "figures\Study_2\B2.4.gph" "figures\Study_2\B2.8.gph", col(3) title("C", position(11))

. 
end of do-file

.  do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. 
. *coefplot
. coefplot (m2a, label(Autocracy)) ///
> (m4a, label(Anocracy)) ///
> (m8a, label(New Democracy)), xline(0) ///
> title("", size(medsmall) span) xtitle("Average Marginal Effects", size(small)) ylabel(,labsize(vsmall)) xlabel(,labsize(vsmall)) legend(size(vsmall) span) legend(off) title("B", position(11))

. 
end of do-file

.  do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. 
. 
. *coefplot
. coefplot (m2a, label(Autocracy)) ///
> (m4a, label(Anocracy)) ///
> (m8a, label(New Democracy)), xline(0) ///
> title("", size(medsmall) span) xtitle("Average Marginal Effects", size(small)) ylabel(,labsize(vsmall)) xlabel(,labsize(vsmall)) legend(size(vsmall) span) legend(off) title("B", position(11))

. 
. 
. graph save "figures\Study_2\Figure_2_AME_Regime_Type.gph", replace
file figures\Study_2\Figure_2_AME_Regime_Type.gph saved

. 
end of do-file

.  do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. 
. *coefplot
. coefplot (m1, label(Autocracy * Peacekeepers) lcolor(black) lwidth(thick) lpattern(dash))  (m2, label(Anocracy * Peacekeepers)) (m3, label(Democracy * Peacekeepers)), xline(0) xtitle(Average marginal effects) l
> egend(off) title("A", position(11))

. 
. graph save "figures\Study_2\Figure_2_original.gph", replace
file figures\Study_2\Figure_2_original.gph saved

. 
. 
end of do-file

. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. 
. 
. graph combine "figures\Study_2\Figure_2_original.gph" "figures\Study_2\Figure_2_AME_Regime_Type.gph", col(2)

. graph save "figures\Study_2\Figure_work_around.gph", replace
file figures\Study_2\Figure_work_around.gph saved

. 
. 
end of do-file

.  do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. 
. *coefplot
. coefplot (m1, label(Autocracy * Peacekeepers) lcolor(black) lwidth(thick) lpattern(dash))  (m2, label(Anocracy * Peacekeepers)) (m3, label(Democracy * Peacekeepers)), xline(0) xtitle(Average marginal effects) l
> egend(off) title("A", position(11))

. 
. graph save "figures\Study_2\Figure_2_original.gph", replace
file figures\Study_2\Figure_2_original.gph saved

. 
end of do-file

. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. 
. *coefplot
. coefplot (m1, label(Autocracy * Peacekeepers)) ///
> (m2, label(Anocracy * Peacekeepers)) ///
> (m3, label(Democracy * Peacekeepers)), xline(0) ///
> title("", size(medsmall) span) xtitle("Average Marginal Effects", size(small)) ylabel(,labsize(vsmall)) xlabel(,labsize(vsmall)) legend(off) title("B", position(11))

. 
end of do-file

. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. *coefplot
. coefplot (m1, label(Autocracy * Peacekeepers)) ///
> (m2, label(Anocracy * Peacekeepers)) ///
> (m3, label(Democracy * Peacekeepers)), xline(0) ///
> title("", size(medsmall) span) xtitle("Average Marginal Effects", size(small)) ylabel(,labsize(vsmall)) xlabel(,labsize(vsmall)) legend(off) title("B", position(11))

. 
. graph save "figures\Study_2\Figure_2_original.gph", replace
file figures\Study_2\Figure_2_original.gph saved

. 
end of do-file

. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. 
. *coefplot
. coefplot (m2a, label(Autocracy)) ///
> (m4a, label(Anocracy)) ///
> (m8a, label(Democracy, corrected)), xline(0) ///
> title("", size(medsmall) span) xtitle("Average Marginal Effects", size(small)) ylabel(,labsize(vsmall)) xlabel(,labsize(vsmall)) legend(off) title("B", position(11))

. 
. 
. graph save "figures\Study_2\Figure_2_AME_Regime_Type.gph", replace
file figures\Study_2\Figure_2_AME_Regime_Type.gph saved

. 
end of do-file

. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. 
. *coefplot
. coefplot (m1, label(Autocracy * Peacekeepers)) ///
> (m2, label(Anocracy * Peacekeepers)) ///
> (m3, label(Democracy * Peacekeepers)), xline(0) ///
> title("", size(medsmall) span) xtitle("Average Marginal Effects", size(small)) ylabel(,labsize(vsmall)) xlabel(,labsize(vsmall)) legend(off) title("B", position(11))

. 
. graph save "figures\Study_2\Figure_2_original.gph", replace
file figures\Study_2\Figure_2_original.gph saved

. 
end of do-file

. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. coefplot (m1, label(Autocracy * Peacekeepers) lcolor(black) lwidth(thick) lpattern(dash))  (m2, label(Anocracy * Peacekeepers)) (m3, label(Democracy * Peacekeepers)), xline(0) xtitle(Average marginal effects)

. 
end of do-file

. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. 
. graph combine "figures\Study_2\Figure_2_original.gph" "figures\Study_2\Figure_2_AME_Regime_Type.gph", col(2)

. graph save "figures\Study_2\Figure_work_around.gph", replace
file figures\Study_2\Figure_work_around.gph saved

. 
. 
. graph combine "figures\Study_2\Figure_work_around.gph" "figures\Study_2\Figure_B3_AME_by_troop_rounded.gph", col(1)

. 
. 
end of do-file

. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. 
. 
. graph combine "figures\Study_2\B2.2.gph" "figures\Study_2\B2.4.gph" "figures\Study_2\B2.8.gph", col(3) title("C", position(11))

. graph save "figures\Study_2\Figure_B3_AME_by_troop_rounded.gph", replace
file figures\Study_2\Figure_B3_AME_by_troop_rounded.gph saved

. 
end of do-file

. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. 
. graph combine "figures\Study_2\Figure_2_original.gph" "figures\Study_2\Figure_2_AME_Regime_Type.gph", col(2)

. graph save "figures\Study_2\Figure_work_around.gph", replace
file figures\Study_2\Figure_work_around.gph saved

. 
. 
. graph combine "figures\Study_2\Figure_work_around.gph" "figures\Study_2\Figure_B3_AME_by_troop_rounded.gph", col(1)

. 
. 
end of do-file

. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. 
. graph combine "figures\Study_2\Figure_work_around.gph" "figures\Study_2\Figure_B3_AME_by_troop_rounded.gph", col(1)

. graph export "figures\Study_2\Figure_2_Main_Paper.png", as(png) name("Graph") replace
(file figures\Study_2\Figure_2_Main_Paper.png not found)
file figures\Study_2\Figure_2_Main_Paper.png saved as PNG format

. 
end of do-file

. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. 
. *coefplot
. coefplot (m1, label(Autocracy * Peacekeepers)) ///
> (m2, label(Anocracy * Peacekeepers)) ///
> (m3, label(Democracy * Peacekeepers)), xline(0) ///
> title("", size(medsmall) span) xtitle("Average Marginal Effects", size(small)) ylabel(,labsize(vsmall)) xlabel(,labsize(vsmall)) legend(off) title("A", position(11))

. 
. graph save "figures\Study_2\Figure_2_original.gph", replace
file figures\Study_2\Figure_2_original.gph saved

. 
end of do-file

. do "C:\Users\ba72loko\AppData\Local\Temp\STD14e0_000000.tmp"

. 
. *------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------*
. * Figure 2 Main Paper
. *------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------*
. 
. 
. graph combine "figures\Study_2\Figure_2_original.gph" "figures\Study_2\Figure_2_AME_Regime_Type.gph", col(2)

. graph save "figures\Study_2\Figure_work_around.gph", replace
file figures\Study_2\Figure_work_around.gph saved

. 
. 
. graph combine "figures\Study_2\Figure_work_around.gph" "figures\Study_2\Figure_B3_AME_by_troop_rounded.gph", col(1)

. graph export "figures\Study_2\Figure_2_Main_Paper.png", as(png) name("Graph") replace
file figures\Study_2\Figure_2_Main_Paper.png saved as PNG format

. 
end of do-file

. clear all

. exit, clear
